DeepSeek V4 is a family of open-weight Mixture of Experts large language models developed by DeepSeek, a Hangzhou-based AI research lab. Released in preview on April 24, 2026, a launch covered by TechCrunch and Al Jazeera the same day, V4 comprises two variants:[10][16] DeepSeek-V4-Pro, with 1.6 trillion total parameters (49 billion active per token), and DeepSeek-V4-Flash, with 284 billion total parameters (13 billion active per token).[1] Both models support a one-million-token context window and are available as open weights under the MIT license.[1][2] The release introduced a hybrid attention architecture that dramatically reduces inference costs at long contexts, set new low price points for frontier-class models, and was the first DeepSeek release validated for inference on Huawei Ascend processor infrastructure.[8][12] DeepSeek's own technical report tempers the comparison, stating that V4 "falls marginally short of GPT-5.4 and Gemini-3.1-Pro, suggesting a developmental trajectory that trails state-of-the-art frontier models by approximately 3 to 6 months."[5][17] On July 31, 2026 DeepSeek put the first official (non-preview) build of the family into public beta, DeepSeek-V4-Flash-0731, and published its weights on Hugging Face the same day under the MIT license; that build is covered in detail at DeepSeek V4-Flash.[7][32][51] The official DeepSeek V4-Pro build still had not shipped as of August 1, 2026, and the deepseek-v4-pro API continued to serve the April preview.[6][7]

Background

DeepSeek was founded in 2023 as a research arm of the Chinese quantitative hedge fund High-Flyer Capital Management. The lab became globally known after releasing DeepSeek-V3 in December 2024, a 671-billion-parameter MoE model that outperformed or matched several leading Western models at a fraction of the training cost. Its companion reasoning model, DeepSeek-R1, released in January 2025, demonstrated that strong chain-of-thought reasoning could be elicited through reinforcement learning without relying solely on supervised distillation from proprietary models. The two releases together caused a brief shock in global financial markets, sent Nvidia stock down sharply on January 27, 2025, and intensified discussions about AI export controls.

Subsequent updates extended the V3 line through 2025. DeepSeek-V3-0324 arrived in March 2025 with improvements to AIME (+19.8 points) and GPQA (+9.3 points). DeepSeek-R1-0528 in May 2025 pushed reasoning further through increased post-training compute. DeepSeek-V3.1 in August 2025 introduced a hybrid architecture supporting both thinking and non-thinking modes within a single model and lifted SWE-bench Verified scores to 66.0. DeepSeek-V3.1-Terminus followed in September 2025 with agent capability improvements. DeepSeek-V3.2-Exp, also released in September 2025, introduced DeepSeek Sparse Attention as an experimental long-context optimization. The official V3.2 launched on December 1, 2025, with 685 billion total parameters.[25] V3.2 was billed as the official successor to V3.2-Exp; a parallel release, V3.2-Speciale, attained gold-medal-level results on IMO, CMO, ICPC World Finals, and IOI 2025.[25][26]

V4 represents the first architectural ground-up redesign since V3. The total parameter count roughly doubles V3.2, active parameters grow from 37 billion to 49 billion (Pro) or 13 billion (Flash), and the context window quadruples from 256K to 1M tokens. The underlying attention mechanism is entirely new, replacing the Multi-head Latent Attention that had defined the V3 line.[5]

The release came after a publicly reported delay. 36Kr's launch-day commentary described V4 as fifteen months late, with the interval spent migrating the stack from CUDA to Huawei's CANN framework and extending the context window from 128K to 1M tokens.[30] ChinaTalk also reported on internal funding decisions that pushed multimodal capability to a future generation, and on departures of senior staff to Tencent, ByteDance, Xiaomi, and other Chinese tech firms during 2025.[15]

How is DeepSeek V4 built? Architecture

Model variants

DeepSeek V4 ships in two sizes:

Variant	Total parameters	Active parameters	Context	Precision	Size on disk
DeepSeek-V4-Flash	284B	13B	1M tokens	FP8 + FP4 mixed	160 GB
DeepSeek-V4-Pro	1.6T	49B	1M tokens	FP8 + FP4 mixed	865 GB

Both models also ship in base (pre-training checkpoint) variants. The instruct variants support three reasoning modes: Non-Think (fast, no extended reasoning), Think High (deliberate logical analysis), and Think Max (maximum reasoning depth). DeepSeek recommends a minimum 384K-token context window when using Think Max mode so that long chains of thought are not truncated.[1]

The "1M" figure is exact rather than approximate: the model catalog DeepSeek publishes for OpenAI Codex declares a context window of 1,048,576 tokens (2^20) for both deepseek-v4-flash and deepseek-v4-pro, and instructs Codex to treat 95 percent of that as effectively usable.[34][47] Maximum output is 384K tokens for both models.[6]

Hybrid attention: CSA and HCA

The central architectural innovation in V4 is a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). Both are departures from the Multi-head Latent Attention (MLA) used in V3 and from the DeepSeek Sparse Attention introduced in V3.2-Exp.[18]

In CSA, a learned token-level compressor consolidates every m tokens along the sequence dimension into a single key-value entry through softmax-gated pooling with a learned positional bias. Queries then attend over these compressed KV representations using DeepSeek Sparse Attention, which selects only the top-k most relevant compressed blocks via a Lightning Indexer component implemented in FP4 precision. A complementary sliding window layer covers the most recent n_win tokens for local context. The net effect is a 4x compression of the KV cache along the sequence axis.[5][18]

HCA is more aggressive. It consolidates every m' tokens (where m' is considerably larger than m, with a 128x compression ratio cited in the technical report) into a single KV entry and applies dense attention over those consolidated entries.[5] The high compression ratio alone delivers efficiency gains, without requiring the sparse selection step that CSA uses.

Layers in V4-Pro's 61-layer stack use these mechanisms in a specific pattern: layers 0 and 1 apply HCA only, layers 2 through 60 alternate between CSA and HCA, and a final multi-token-prediction (MTP) block uses sliding-window attention only.[5] This layered scheme avoids capacity waste by giving different layers different attention patterns suited to local and global context retrieval.

The result at 1M-token context is dramatic. According to the V4 technical report, DeepSeek-V4-Pro requires only 27% of the per-token inference FLOPs and 10% of the KV cache memory that V3.2 would need for the same context length. DeepSeek-V4-Flash is even leaner at 10% of FLOPs and 7% of KV cache relative to V3.2.[5] From V3.2 to V4-Pro, KV cache memory is reduced by approximately 9.5x to 13.7x across context lengths, according to figures cited by The Register.[8] Compared with a standard Grouped-Query Attention baseline (GQA-8), V4's KV cache is roughly 2% of the equivalent GQA-8 footprint.[5]

Manifold-Constrained Hyper-Connections (mHC)

V4 replaces standard residual connections with Manifold-Constrained Hyper-Connections (mHC). The residual stream width is expanded by a factor of four (n_hc = 4 in both model variants). The residual mapping matrices are constrained to the Birkhoff polytope, the set of doubly stochastic matrices, using the Sinkhorn-Knopp algorithm with a maximum of 20 iterations during training. This constraint bounds the spectral norm of the mapping matrices at 1, which prevents signal amplification through the network and improves training stability without sacrificing model expressivity.[5]

The abstract of the technical report, posted to arXiv on April 26, 2026 as arXiv:2606.19348, lists mHC as one of three headline upgrades alongside the CSA/HCA hybrid attention and the Muon optimizer, describing it as an enhancement of "conventional residual connections."[45] The Hugging Face model card uses the same framing, saying mHC is incorporated "to strengthen conventional residual connections, enhancing stability of signal propagation."[2] mHC has since drawn follow-on academic work of its own, including papers on accelerating the Birkhoff projection and on whether the 20 Sinkhorn-Knopp iterations are necessary.

Muon optimizer

DeepSeek-V4 adopts the Muon optimizer for the majority of its parameters. Muon orthogonalizes gradient updates using Newton-Schulz iterations. V4 uses a hybrid schedule: 8 convergence iterations followed by 2 stabilization iterations. Embeddings, prediction heads, biases, and RMSNorm weights use AdamW. According to DeepSeek's technical report, Muon speeds convergence and improves training stability at trillion-parameter scale, and the hybrid Muon plus AdamW configuration was chosen after ablation studies on smaller model sizes.[5]

Precision and quantization

Expert weights in the MoE layers use FP4 precision with quantization-aware training, roughly halving the weight storage footprint versus FP8. Non-expert parameters use FP8. The KV cache stores most entries in FP8, with BF16 reserved only for the rotary positional embedding (RoPE) dimensions where higher precision matters. Base model checkpoints use FP8 throughout, while instruct models combine FP4 (experts) with FP8 (everything else).[5] FP4 adoption at the expert level is a key reason V4-Pro's 865 GB download size is manageable despite 1.6 trillion nominal parameters; an equivalent FP8-only model would be roughly 60% larger.

The Register specifically noted V4's adoption of MXFP4 (microscaling FP4) as a step away from Nvidia-specific FP8 formats, framing it as a deliberate move toward hardware portability across accelerator vendors.[8]

Training stability mechanisms

Two techniques address instability specific to large-scale MoE training. Anticipatory Routing uses historical routing parameters from earlier in training (theta_{t minus delta_t}) to compute token assignments, decoupling backbone and router gradient updates so they do not interfere. SwiGLU Clamping constrains the linear components of SwiGLU activations to the range [-10, 10] and gates to a maximum of 10, preventing gradient explosions in expert layers when individual activations occasionally produce outlier values during training.[5]

Training methodology

Pre-training

The technical report states that DeepSeek-V4-Flash was pre-trained on 32 trillion tokens and DeepSeek-V4-Pro on 33 trillion, so the larger corpus belongs to Pro. Both are described as diverse and high-quality. Both corpora include long-document data to support the 1M-token context objective. The specific composition of the training data, beyond these summary figures, is not disclosed in the technical report.[5]

The V4 paper emphasizes that long-context training data is curated rather than synthesized, with emphasis on whole-document examples (codebases, books, legal corpora) rather than artificially concatenated short documents. The ratio of long-context to short-context examples is gradually increased during training.[5]

Post-training: specialist-then-distill

V4's post-training pipeline is a two-stage specialist-then-distill approach.

In the first stage, separate specialist models are trained for distinct domains: mathematics, coding, agentic tasks, and instruction following, among others. Each specialist starts from the shared pre-trained base and undergoes domain-specific Supervised Fine-Tuning (SFT) followed by Reinforcement Learning using Group Relative Policy Optimization (GRPO) with domain-tailored reward signals. This produces a set of more than ten domain-expert teacher models, each strong in its area.[5]

In the second stage, a single unified student model is distilled from all teachers simultaneously through On-Policy Distillation (OPD). The student generates its own outputs, then minimizes the reverse KL divergence against whichever teacher is most relevant to the current task's logit distribution. Full-vocabulary logit distillation, rather than top-k, is used for stable gradient estimates.[5] The result is a single inference model that combines the strengths of all domain specialists.

This approach contrasts with V3's more uniform reinforcement learning across domains and allows V4 to optimize more precisely for different task types without maintaining multiple inference models in production.

Post-training is also where DeepSeek made the only change it has shipped to a V4 model since launch. The July 31, 2026 V4-Flash build carries the same architecture and the same parameter count as the April preview and was, in DeepSeek's words, "only re-post-trained."[7]

DSec: training infrastructure for agents

A significant infrastructure component disclosed in the V4 technical report is DeepSeek Elastic Compute (DSec), a Rust-based platform that exposes four execution substrates for reinforcement learning rollouts: function calls, containers, microVMs (Firecracker), and full VMs (QEMU). DSec is designed to run hundreds of thousands of concurrent sandboxes during training, with fast image loading via layered 3FS storage so containers do not incur cold-start delays during rollouts, and preemption-safe trajectory replay so an interrupted rollout can resume without re-running tool calls.[5]

DSec enabled V4's agent-focused post-training to use real tool environments at scale rather than simulated stubs. The interleaved thinking pattern (preserving chain-of-thought across tool calls when tools are present, discarding it across user messages when they are absent) was specifically tuned through DSec rollouts.[5]

Tool-call schema with dedicated tokens

V4 introduces a |DSML| special token that wraps an XML-based tool-call format. This was a deliberate move away from JSON tool-call formats (used by most Western models) because the XML form reduces escaping failures around quoted strings and structured parameters. Parameters are marked with string="true" or string="false" to distinguish quoted strings from numbers and booleans, which the technical report claims reduces parsing errors during multi-turn agent loops.[5]

Benchmark performance

The following scores are for DeepSeek-V4-Pro-Max (the highest-effort inference setting using Think Max mode) unless otherwise noted, taken from DeepSeek's technical report and Hugging Face model card.[2][5] The competitor columns are DeepSeek's own runs of other vendors' models rather than published leaderboard results: the technical report says it re-evaluated them itself to standardise conditions, which is also why some cells are blank.[5]

Coding

Benchmark	V4-Pro-Max	Opus 4.6 Max	GPT-5.4 xHigh	Gemini 3.1 Pro High	K2.6 Thinking
LiveCodeBench Pass@1	93.5	88.8	--	91.7	89.6
Codeforces Rating	3206	--	3168	3052	--
SWE-bench Verified	80.6%	80.8%	--	80.6%	80.2%
SWE-bench Pro	55.4%	57.3%	57.7%	54.2%	58.6%
SWE-bench Multilingual	76.2%	77.5%	--	--	76.7%
Terminal-Bench 2.0	67.9%	65.4%	75.1%	68.5%	66.7%
MCPAtlas Public	73.6	73.8	67.2	69.2	66.6
Toolathlon	51.8	47.2	54.6	48.8	50.0

V4-Pro-Max's Codeforces rating of 3206 was the highest score achieved by any AI model at the time of release, surpassing GPT-5.4-xHigh's 3168 and Gemini's 3052.[2][5] Estimates put the score at roughly the equivalent of rank 23 on Codeforces globally. On SWE-bench Verified, V4-Pro is at near parity with Claude Opus 4.6 (80.8%), Gemini 3.1 Pro (80.6%) and Kimi K2.6 Thinking (80.2%); DeepSeek's table reports no figure for GPT-5.4 xHigh on that benchmark. V4-Pro-Max leads all six models on LiveCodeBench at 93.5, and trails GPT-5.4 xHigh by seven points on Terminal-Bench 2.0.[2]

Mathematics and science

Benchmark	V4-Pro-Max	Opus 4.6 Max	GPT-5.4 xHigh	Gemini 3.1 Pro High
GPQA Diamond	90.1%	91.3%	93.0%	94.3%
HMMT 2026 Feb	95.2%	96.2%	97.7%	94.7%
IMOAnswerBench	89.8%	75.3%	91.4%	81.0%
Apex	38.3%	34.5%	54.1%	60.9%
Apex Shortlist	90.2%	85.9%	78.1%	89.1%
Putnam 2025	120/120	--	--	--
GSM8K (base)	92.6%	--	--	--
MATH (base)	64.5%	--	--	--

V4 achieves a perfect score on Putnam 2025, the undergraduate mathematics competition dataset, though DeepSeek reports that result under a hybrid formal-informal regime with heavy compute scaling rather than as a standard evaluation.[5] On GPQA Diamond, a PhD-level science benchmark, V4 scores 90.1%, the lowest of the four, against Gemini 3.1 Pro's 94.3%.[2] The pattern across this block is that V4-Pro is competitive on competition mathematics but well behind on Apex, where GPT-5.4 xHigh and Gemini 3.1 Pro score 54.1 and 60.9 against its 38.3.[2]

General knowledge

Benchmark	V4-Pro-Max	Opus 4.6 Max	GPT-5.4 xHigh	Gemini 3.1 Pro High
MMLU-Pro	87.5%	89.1%	87.5%	91.0%
SimpleQA Verified	57.9%	46.2%	45.3%	75.6%
Chinese SimpleQA	84.4%	76.4%	76.8%	85.9%
HLE	37.7%	40.0%	39.8%	44.4%

On MMLU-Pro, V4-Pro ties GPT-5.4 xHigh at 87.5 and trails Claude Opus 4.6 and Gemini 3.1 Pro. On SimpleQA Verified (a factual accuracy benchmark), V4-Pro's 57.9% clears both Claude Opus 4.6 at 46.2% and GPT-5.4 xHigh at 45.3%, but Gemini 3.1 Pro leads the group by a wide margin at 75.6%.[2] DeepSeek's own technical paper acknowledges that V4 "trails state-of-the-art frontier models by approximately three to six months," placing it roughly at parity with mid-2025 frontier models such as GPT-5.2, Gemini 3.0 Pro, and Claude Opus 4.5.[5]

Long-context

Benchmark	V4-Pro-Max	Opus 4.6 Max	Gemini 3.1 Pro High
MRCR 1M (MMR)	83.5	92.9	76.3
CorpusQA 1M	62.0%	71.7%	53.8%
LongBench-V2 (base)	51.5%	--	--
BrowseComp	83.4%	83.7%	85.9%

At 1M-token retrieval (MRCR), V4-Pro scores 83.5 MMR, trailing Claude Opus 4.6's 92.9 but ahead of Gemini 3.1 Pro's 76.3.[2] The Hugging Face blog reports that V4-Pro stays above 0.82 accuracy through 256K tokens on MRCR with 8 needles and holds at 0.59 at 1M tokens.[3]

The July 2026 agent-benchmark update

DeepSeek published nine agent-oriented scores alongside the V4-Flash-0731 release on July 31, 2026, and its framing was that the re-post-trained Flash build now beats the larger V4-Pro-Preview across every one of them.[7][32] A sample of the change:

Benchmark	V4-Flash-0731	V4-Flash-Preview	V4-Pro-Preview
Terminal-Bench 2.1	82.7	61.8	72.1
DeepSWE	54.4	7.3	12.8
Toolathlon-Verified	70.3	49.7	55.9

The full nine-benchmark table, the competitor columns, and the reception of the result are covered at DeepSeek V4-Flash. Note that the V4-Pro figures here are for the preview build that DeepSeek was serving on deepseek-v4-pro in July 2026, not for an official Pro release.[7]

Reading DeepSeek's published scores

DeepSeek attaches two methodology notes to the July 2026 numbers, and both limit how far they can be compared with published leaderboards.[7]

First, the public code-agent tasks were run inside DeepSeek's own scaffold, which it calls the DeepSeek Harness in minimal mode and which it described as "to be released soon" on the day it published the scores, at the max effort level with top_p 0.95 and temperature 1.0.[7] Agent benchmark results depend heavily on the harness driving the model (retry policy, tool set, context management), so a score produced by an unreleased first-party framework is not interchangeable with one produced by a public one.

Second, two of the nine benchmarks are DeepSeek's own. DSBench-FullStack is described as an internal full-stack development test set and DSBench-Hard as an internal coding-agent hard-problem set, neither of which is published, so no outside party can reproduce those rows.[7] The Toolathlon row is likewise labelled "Toolathlon verified" rather than the full public suite.

How much does DeepSeek V4 cost? Pricing

DeepSeek charges per token on its official API. V4-Pro launched behind a 75% introductory discount, which DeepSeek extended twice and then made permanent: on May 22, 2026 the company announced it was "making our discount permanent," and the discounted rates became the standing list prices.[40][41] DeepSeek's live pricing documentation, in both its English and Chinese versions, now shows $0.435 and $0.87 per million tokens as the plain rates for deepseek-v4-pro, with no promotional label and no expiry date attached.[6][31]

Official DeepSeek API pricing

Rates below are DeepSeek's first-party list prices as published on its pricing page, re-checked against the English and Chinese versions on August 1, 2026. Figures are per one million tokens.[6][31]

Model	Input (cache hit)	Input (cache miss)	Output	Max output	Concurrency
`deepseek-v4-flash`	$0.0028 / 1M	$0.14 / 1M	$0.28 / 1M	384K tokens	2,500
`deepseek-v4-pro`	$0.003625 / 1M	$0.435 / 1M	$0.87 / 1M	384K tokens	500

DeepSeek's Chinese-language page quotes the same rates in yuan: 0.02, 1 and 2 per million tokens for Flash, and 0.025, 3 and 6 for Pro.[31] Concurrency limits are counted per account rather than per API key, and requests beyond the limit return HTTP 429; DeepSeek offers capacity expansion on request at no extra charge, and a user_id parameter that isolates KV cache, scheduling, and content-safety handling between a customer's own end users.[35]

How V4-Pro arrived at those numbers:

Date	Event
April 24, 2026	V4 preview launch. V4-Pro list price $1.74 input (cache miss) / $3.48 output per 1M tokens.[6][28]
April 25, 2026	75% discount announced, initially through May 5, 2026, 15:59 UTC.[49]
April 29, 2026	Discount extended to May 31, 2026, 15:59 UTC.[49]
May 22, 2026	DeepSeek announces the discount is permanent; $0.435 / $0.87 become the standing rates.[40][41][42]

Cache-hit pricing represents automatic context caching. DeepSeek applies context caching transparently without requiring developers to declare cache keys or set TTLs, and the Responses API rejects the prompt_cache_key and prompt_cache_retention parameters for that reason.[6][33] Artificial Analysis calculated the resulting cache-hit discount at roughly 98 percent, against the 90 percent most of the industry offers.[37]

Peak and off-peak pricing

Footnote (2) on DeepSeek's pricing page, present identically in the English and Chinese versions, states that the API "will soon adopt a peak/off-peak pricing policy" under which prices during peak hours will be 2x the regular prices, applicable to all billing items. Peak hours are defined as 09:00 to 12:00 and 14:00 to 18:00 Beijing time (UTC+8) daily, and the effective date is "subject to the official announcement."[6][31] The wording was unchanged on August 1, 2026: the policy had still not taken effect and no start date had been published, so the rates in the table above are the ones actually charged. The July 31 official Flash launch came and went without it being switched on.

Doubling would put V4-Flash output at 4 yuan and V4-Pro output at 12 yuan per million tokens during those windows. BigGo Finance reported that Chinese developers objected that the proposed peak window covers most of a domestic working day, effectively favoring customers in other time zones, while others argued that a doubled DeepSeek rate would still undercut competing APIs.[46]

The scheme inverts DeepSeek's earlier experiment with time-of-day pricing. From February 26, 2025 the company ran off-peak discounts instead, cutting DeepSeek-V3 by 50 percent and DeepSeek-R1 by 75 percent between 16:30 and 00:30 UTC daily.[43] That program ended on September 5, 2025, 16:00 UTC, when DeepSeek moved to a new flat price list.[48]

Comparison with competing models

List prices below are as of the dates given, and competitor rates move frequently; treat the non-DeepSeek rows as a snapshot rather than a live quote.

Model	Input ($/1M)	Output ($/1M)	Context
DeepSeek V4-Flash	$0.14	$0.28	1M tokens
DeepSeek V4-Pro	$0.435	$0.87	1M tokens
Gemini 3.1 Pro	$2.00	$12.00	1M tokens
GPT-5.4	$2.50	$15.00	1M tokens
GPT-5.5	$5.00	$30.00	1M tokens
Claude Opus 4.6	$5.00	$25.00	1M tokens
Claude Opus 4.7	$5.00	$25.00	1M tokens

At its permanent rates, V4-Pro output costs $0.87 per million tokens versus $25 for Claude Opus 4.7 and $30 for GPT-5.5, roughly a 29x to 34x gap in DeepSeek's favor.[6] Before the May 2026 cut, at the original $3.48 output rate, the gap was closer to 7x to 9x. VentureBeat described V4 as offering "near state-of-the-art intelligence at 1/6th the cost" of top Western models.[19] Artificial Analysis, running its full Intelligence Index suite on V4-Pro at first-party pricing after the cut, put the cost of one full run at about $268 in May 2026 (its published figure on the v4.1 index as of July 31 was $176.34), which it calculated as roughly 3x cheaper than Gemini 3.1 Pro Preview, 12x cheaper than GPT-5.5 at extra-high effort, and 19x cheaper than Claude Opus 4.7.[42]

Which API formats does DeepSeek V4 support?

The DeepSeek API accepts three wire formats. OpenAI ChatCompletions and Anthropic Messages requests both work against https://api.deepseek.com (Anthropic-format calls use the /anthropic path) and have done since the April 2026 launch.[1] The 0731 Flash build added a third: OpenAI's Responses API, the stateful, tool-oriented protocol that OpenAI's Codex clients speak.[7][33]

Support for the newer protocol is narrower than the model family. DeepSeek's documentation states in three places, the pricing page footnote, the Responses API guide, and the Codex integration page, that the Responses API and the Codex configuration cover deepseek-v4-flash only, and that deepseek-v4-pro support is expected in early August 2026.[6][33][34]

DeepSeek publishes a ~/.codex/config.toml fragment that registers a [model_providers.deepseek] block with wire_api = "responses", plus a models.json catalog that declares the model's context window and reasoning levels so Codex treats it like a built-in model. A one-line setup script for macOS, Linux, and Windows writes both files, backing up the existing configuration first and validating the syntax before writing. The same configuration serves the Codex CLI, the ChatGPT desktop app, and the Codex extension for Visual Studio Code.[34]

The implementation is a deliberate subset rather than a full clone. It is stateless, so previous_response_id and conversation are unsupported and store always returns false; background, metadata, include, prompt, service_tier, safety_identifier, and context_management are not implemented either. truncation is unsupported, so a request that exceeds the context window returns HTTP 400 instead of being silently trimmed. Among tools, function and web_search work, custom is accepted only for the apply_patch name that Codex needs, and file_search, code_interpreter, computer_use, and Model Context Protocol tools are ignored. Image and file inputs are not supported: an input_image part does not raise an error but is swapped for placeholder text. Unsupported parameters are silently ignored so that existing clients connect without modification.[33]

DeepSeek documents a separate integration for Claude Code at the /anthropic endpoint, in which V4-Pro fills the Opus and Sonnet roles and V4-Flash is mapped to the Haiku and subagent roles.[7]

Model variants

As of August 1, 2026 the DeepSeek API exposes exactly two model ids, deepseek-v4-flash and deepseek-v4-pro, but four distinct named builds have been released or announced under the V4 name. The table below separates them, because DeepSeek's documentation and its benchmark charts use the longer names while the API uses the short ids.

Name	What it is	API model id	Open weights	Status on 2026-08-01
DeepSeek-V4-Flash-Preview	April 24, 2026 preview build, 284B total / 13B active	`deepseek-v4-flash` (until July 31)	Yes, MIT, on Hugging Face	Superseded on the API; repository frozen at its June 22, 2026 state[7][44]
DeepSeek-V4-Flash-0731	Official Flash build, re-post-trained, same architecture and size	`deepseek-v4-flash`	Yes, MIT, on Hugging Face from July 31, 2026	Public beta from July 31, 2026[7][32][51]
DeepSeek-V4-Pro-Preview	April 24, 2026 preview build, 1.6T total / 49B active	`deepseek-v4-pro`	Yes, MIT, on Hugging Face	Still the build being served[7][2]
DeepSeek-V4-Pro (official)	Announced successor to the Pro preview	expected on `deepseek-v4-pro`	Not announced	Not released; DeepSeek says it will "follow soon"[7]
DeepSeek-V4-Flash-Base / V4-Pro-Base	Pre-training checkpoints, FP8 throughout	not served	Yes, MIT, on Hugging Face	Available[2][44]

Two details are easy to misread. DeepSeek's pricing page lists the model version for deepseek-v4-pro simply as "DeepSeek-V4-Pro," but the change log entry published the same day states that the V4-Pro API was not changed and that the official V4-Pro release will follow later, and DeepSeek's own July comparison chart labels the Pro column "V4-Pro-Preview."[6][7][32] And because the 0731 update reused the existing model id, callers who had hard-coded deepseek-v4-flash picked up different behavior on July 31 without changing a line of code, which matters when comparing evaluation runs across that date.[7]

A third set of repository names is not a set of builds at all. DeepSeek-V4-Pro-DSpark and DeepSeek-V4-Flash-DSpark, published on Hugging Face on June 27, 2026, each open with the same disclaimer: the repository "is not a new model. It is the same checkpoint with an additional speculative decoding module attached."[52][53] DSpark is a draft-model method from DeepSeek's DeepSpec codebase, which trains and evaluates draft models for speculative decoding; vLLM and SGLang enable it with a flag rather than by loading different base weights.[52][54] The July 31 Flash build ships the module by default: DeepSeek-V4-Flash-0731 states that it "has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached," and its config.json carries the dspark_* keys to prove it.[51]

DeepSeek-V4-Pro

V4-Pro is the larger of the two variants, with 1.6 trillion total parameters and 49 billion active per token. It uses FP4 for expert weights and FP8 for other layers, resulting in an 865 GB checkpoint.[2] It is aimed at demanding tasks: complex multi-step reasoning, advanced coding, scientific analysis, and long-document comprehension. On agentic benchmarks it rivals or approaches the performance of Claude Opus 4.6 and GPT-5.4. The V4-Pro Hugging Face page listed more than 1.06 million downloads in the first month after release; by July 31, 2026 the repository showed roughly 1.64 million downloads in the preceding 30 days and about 5,400 likes.[2]

Thinking-effort handling differs between the two models. DeepSeek's documentation maps a requested effort of low on deepseek-v4-pro up to an actual effort of high, so Pro has no genuine low-effort mode; the company said it would revise that mapping in early August 2026.[36]

Pro did not receive the July 31 update that Flash did. The deepseek-v4-pro id was still answering with the April preview on August 1, 2026, the announced official Pro build had no date, and Responses API and Codex support for the id was still forecast for early August.[6][7] The model is treated in full at DeepSeek V4-Pro.

DeepSeek-V4-Flash

V4-Flash has 284 billion total parameters with 13 billion active per token, weighing 160 GB.[1] According to DeepSeek, "reasoning capabilities closely approach V4-Pro" despite the smaller scale, and it runs at substantially lower cost and latency. V4-Flash is positioned as a drop-in replacement for the legacy deepseek-chat and deepseek-reasoner API endpoints, which route to V4-Flash's non-thinking and thinking modes respectively.[1] Those legacy endpoints were retired on July 24, 2026, 15:59 UTC, three months after the preview launch, leaving the two deepseek-v4-* ids as the only names on the API.[1][7]

One week later, on July 31, 2026, DeepSeek put the official Flash build into public beta as DeepSeek-V4-Flash-0731 and published its weights the same day. It is the main article subject at DeepSeek V4-Flash, which covers the benchmark jump, the independent measurements, the weights release, and the Codex integration in detail.[7][32][50][51]

Base models

Both Pro and Flash ship with base (pre-training checkpoint) variants in addition to instruction-tuned versions. Base models are available as DeepSeek-V4-Flash-Base and DeepSeek-V4-Pro-Base and are intended for downstream fine-tuning. Both base checkpoints use FP8 precision throughout, without the FP4 expert quantization applied to instruct models.[5]

Is DeepSeek V4 open source? Release and licensing

DeepSeek released V4 weights under the MIT License, one of the most permissive open source licenses. Announcing the launch on April 24, 2026, DeepSeek wrote that "DeepSeek-V4 Preview is officially live and open-sourced," welcoming developers to "the era of cost-effective 1M context length."[1] The MIT terms allow commercial use, redistribution, and modification without royalties or restrictions.[2] The models are available for download on Hugging Face under the deepseek-ai organization.[2] V4-Pro accumulated more than one million downloads in its first month, and the V4 collection was the most-downloaded large language model collection on Hugging Face in late April and early May 2026.[4]

MIT is the license across all seven V4 repositories DeepSeek has published, but it is not declared uniformly. Five of them, the two instruct checkpoints, the two DSpark repositories and the July 2026 Flash build, carry the mit license tag in their Hub metadata and repeat it in their model cards. The two base checkpoints carry neither a Hub license tag nor a model card, and ship the licence only as a LICENSE file in the repository. Third-party listings that describe V4 as Apache 2.0 are wrong.[2][44][51][52][53]

The official Flash build briefly looked as though it would stay API-only, because DeepSeek did not publish it as a revision of the existing deepseek-ai/DeepSeek-V4-Flash repository, which is still frozen at its June 22, 2026 state and still opens with the sentence "We present a preview version of DeepSeek-V4 series."[44] It went into a new repository instead, deepseek-ai/DeepSeek-V4-Flash-0731, whose release commit landed on July 31, 2026, the same day as the public beta and the change log entry.[7][51] The file listing shows 48 safetensors shards totalling roughly 167 GB, an mit license tag, and the model card's own description of the build as "the official release of DeepSeek-V4-Flash, superseding the preview version."[51][55] Artificial Analysis had written on the day of the API launch that DeepSeek "is expected to release the model's full weights in the coming weeks"; the wait was hours, not weeks.[37]

Two figures on that repository do not match the specification table above, and readers comparing the two should know why. The Hub sidebar reports 304B parameters rather than 284B, and the repository is 167 GB across 48 shards where the preview repository is 160 GB across 46.[44][51][55] Only the storage step is the DSpark draft module. The parameter figure is an accounting artifact: DeepSeek-V4-Flash-DSpark carries the same module, the same 48 shards and byte-identical storage of 166,886,535,336 bytes, yet Hugging Face reports 165.3B for it against 304.2B for the 0731 repository. Two repositories holding the same bytes cannot differ by 139 billion parameters. The gap lies entirely in how each declares its tensors: the 0731 repository reports 296,352,743,424 INT8 elements and no E8M0 tensors, while the DSpark repository reports exactly half that many INT8 elements plus 9,261,408,000 E8M0 tensors, which the shared config.json identifies as quantisation scales through "scale_fmt": "ue8m0" rather than as weights.[44][51] Neither sidebar figure is a parameter count. DeepSeek's own statements agree that the model did not change: the change log says the 0731 build "keeps the same model architecture and size as DeepSeek-V4-Flash-Preview, and was only re-post-trained," the DeepSeek-V4-Flash-DSpark card gives 284B total and 13B activated for the same checkpoint, and the 0731 config.json still declares 256 routed experts, 6 experts per token, one shared expert, and a 1,048,576-token maximum position count.[7][51][53]

The practical consequence is that the open-weight checkpoints and the served API models are the same artifacts again on both ids: deepseek-v4-flash serves the 0731 build and deepseek-v4-pro serves the preview, and both have public weights.

The technical report is available as a PDF on the Hugging Face model card, titled "DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence."[5] It was also posted to arXiv on April 26, 2026 as arXiv:2606.19348, credited to DeepSeek-AI and several hundred named authors.[45]

DeepSeek provides inference examples for multiple deployment frameworks including Hugging Face Transformers, vLLM, SGLang, and Docker Model Runner.[2] Quantized versions compatible with llama.cpp, Ollama, LM Studio, and Jan are available from the community. Unsloth published a fine-tuning fork on Hugging Face within days of release, and Nvidia published an optimized variant for its NIM inference platform.

Deployment at the 865 GB scale of V4-Pro requires significant hardware. For a rough estimate, running at FP4 plus FP8 mixed precision on a single node typically requires multiple high-memory accelerators, such as several H100 80GB GPUs. V4-Flash at 160 GB is more accessible for organizations with modest compute, fitting within a single 8-GPU H100 or H200 node at full precision and on smaller hardware after quantization.

Capabilities

Agentic coding

V4-Pro showed particular strength on agentic coding tasks. On SWE-bench Verified, it resolves 80.6% of GitHub issues, near parity with Claude Opus 4.6's 80.8% and Gemini 3.1 Pro's 80.6%. On BrowseComp (web browsing and research), it scores 83.4%.[2] V4-Pro-Max's Codeforces rating of 3206 represents the highest competitive programming score by an AI model at the time of release.[5] Developer community testing, as reported by MindStudio and independent developers, put V4-Pro in the top two or three choices for coding use cases in the months following release. A MindStudio survey reported by ghost.codersera found that 52% of surveyed DeepSeek developers were ready to replace their primary coding model with V4-Pro, and another 39% were leaning toward yes.

The technical report also disclosed an internal R&D coding evaluation in which V4-Pro scored 67% pass rate, between Claude Sonnet 4.5 (47%) and Claude Opus 4.5 (70%).[5]

By mid-2026 the ranking inside the family had inverted for agent work specifically. On the nine benchmarks DeepSeek published on July 31, 2026, the re-post-trained 13-billion-active Flash build scored above the 49-billion-active Pro preview on every one, a result DeepSeek presented as evidence that post-training, not parameter count, was the binding constraint on agent performance.[7][32]

Long-context comprehension

The 1M-token context window is fully operational at launch, rather than a theoretical maximum. On the MRCR 1M needle-in-a-haystack benchmark, V4-Pro scores 83.5 MMR, below Claude Opus 4.6 but ahead of other open models.[2] At 1M tokens, V4-Pro's FLOPs requirement is 27% of V3.2's, making long-context inference economically viable.[5] The Hugging Face blog article specifically frames V4 as "a million-token context that agents can actually use," noting that prior 1M-context releases tended to be theoretical maxima with degraded retrieval at high token counts.[3]

Reasoning modes

Both V4 variants support three inference modes. Non-Think mode produces fast responses without extended reasoning chains, suitable for routine queries. Think High mode engages deliberate analysis with a moderate token budget for thinking. Think Max mode pushes reasoning to its full extent, requiring a minimum 384K-token context window to accommodate extended chain-of-thought traces.[1]

In the two older wire formats the API splits this into two controls rather than one. Thinking mode is toggled with {"thinking": {"type": "enabled"}} or disabled, and it is enabled by default; effort within thinking mode is then set with reasoning_effort (OpenAI format) or output_config.effort (Anthropic format), each accepting low, high, or max, with high as the default. The Responses format collapses both into one field, {"reasoning": {"effort": ...}}, where an effort of none disables thinking altogether. Temperature, top_p, presence_penalty, and frequency_penalty have no effect in thinking mode; they are accepted without error for client compatibility and then ignored.[36]

The interleaved-thinking design preserves reasoning content across tool calls (within a single agent task) but discards it across user message boundaries (in conversational use without tools). This avoids context pollution in chat-style use while supporting long-horizon agent loops.[5] The rule is enforced by the API: between two user messages, reasoning_content from an earlier turn is ignored if the model made no tool call, and must be passed back in every subsequent turn if it did.[36]

Instruction following and multilingual

V4 shows competitive multilingual performance. Chinese SimpleQA scores 84.4%, and the model performs well on Chinese-language tasks. On SimpleQA Verified (English factual accuracy), V4-Pro outperforms Claude Opus 4.6 at 46.2% with a score of 57.9%.[2] Reviewer testing flagged that V4-Pro is less reliable than GPT-5.5 and Claude Opus 4.6 on prompts with many simultaneous structural constraints (precise output schemas, exact word counts, multi-format documents), where the larger Western models maintain higher consistency.

Hardware and infrastructure

Training hardware

DeepSeek's training infrastructure for V4 remains primarily Nvidia-based, according to reporting from ChinaTalk and The Register.[8][15] Earlier DeepSeek models were trained on Nvidia A100 clusters obtained before export controls, and subsequent models used A800 chips. V4's training used Nvidia GPUs, though DeepSeek has not publicly specified which generation. U.S. government officials separately alleged that DeepSeek obtained banned Nvidia Blackwell chips through third-party intermediaries, though this has not been independently confirmed.[15]

Huawei Ascend optimization

A significant V4 development is its validated support for Huawei Ascend 950PR processors for inference.[12] DeepSeek's technical report describes the MegaMoE fused kernel as successfully running on Huawei Ascend hardware, and the report mentions validating a fine-grained Expert Parallel scheme on both Nvidia GPUs and Ascend NPU platforms.[5] Huawei announced full Ascend platform support for V4 models at launch.[13] The company's TileLang domain-specific language reduces CUDA dependency on the inference path, enabling portability to non-Nvidia accelerators.

The Register notes that V4 adopts MXFP4 (microscaling FP4) for post-training and inference, reducing dependence on Nvidia-specific FP8 formats.[8] This was described by technical analysts as a deliberate step toward hardware portability.

For now, the Huawei integration covers inference, not training, with training still primarily on Nvidia hardware.

If V4's Huawei inference optimization proves reliable at scale, it could provide evidence that competitive frontier models can run on non-Nvidia infrastructure, with implications for the effectiveness of U.S. semiconductor export controls. Nvidia CEO Jensen Huang called the prospect of DeepSeek running on Huawei chips "a horrible outcome for America," in a statement reported by The Next Web.

How does DeepSeek V4 compare with frontier models?

Model	Org	Open weights	Params (active)	Input $/1M	Output $/1M	SWE-bench Verified	GPQA Diamond
DeepSeek V4-Pro	DeepSeek	Yes (MIT)	49B (1.6T total)	$0.435	$0.87	80.6%	90.1%
DeepSeek V4-Flash	DeepSeek	Yes (MIT)	13B (284B total)	$0.14	$0.28	--	--
Claude Opus 4.6	Anthropic	No	undisclosed	$5.00	$25.00	80.8%	~91%
Claude Opus 4.7	Anthropic	No	undisclosed	$5.00	$25.00	--	--
GPT-5.5	OpenAI	No	undisclosed	$5.00	$30.00	88.7%	--
Gemini 3.1 Pro	Google	No	undisclosed	~$3.50	~$10.50	80.6%	94.3%
Kimi K2.6	Moonshot AI	Partial	undisclosed (1.1T total)	--	--	--	--
GLM-5.1	Zhipu AI	Partial	undisclosed (754B total)	--	--	--	--

V4-Pro is the largest open-weight model available as of its release date, surpassing Moonshot AI's Kimi K2.6 at 1.1T total parameters and Zhipu AI's GLM-5.1 at 754B. Among Chinese open models, V4-Pro leads across math, coding, and STEM benchmarks.[20] That position eroded over the following quarter: by the end of July 2026 both labs had shipped successors (Kimi K3 and GLM-5.2) that Artificial Analysis rated above V4-Pro on its aggregate index.[37]

Industry impact

China-US AI competition

DeepSeek V4 landed on the same day Reuters reported the U.S. State Department had sent a diplomatic cable to embassies worldwide instructing staff to warn foreign governments about alleged IP theft by DeepSeek and other Chinese AI companies.[13] The concurrent timing, whether coincidental or deliberate, brought significant geopolitical attention to the release.

The Council on Foreign Relations published an analysis that same day arguing V4 "signals a new phase in the U.S.-China AI rivalry," with the competition shifting from raw frontier capability toward economic adoption and global influence, particularly in the Global South.[14]

CFR Senior Fellow Michael C. Horowitz emphasized the adoption race angle: "Success will not just be about having the best-performing models," he wrote, but about having good-enough solutions that deploy cheaply at scale. He noted that Chinese open models already had more downloads on Hugging Face than U.S. equivalents, a dynamic V4's open release was likely to amplify.[14]

CFR Senior Fellow Jessica Brandt raised concerns that V4's capabilities "reflect, at least in part, access to illicitly obtained U.S. intellectual property," citing alleged large-scale model distillation attacks through fake accounts.[14] Anthropic and OpenAI separately alleged that DeepSeek-affiliated actors had created tens of thousands of fake accounts conducting tens of millions of interactions to extract capabilities from frontier U.S. models. DeepSeek has not publicly responded to these allegations.

CFR Senior Fellow Chris McGuire offered a more measured technical assessment, noting that V4 "is not competitive with frontier U.S. models" and that DeepSeek remains significantly dependent on U.S. semiconductor technology.[14] He flagged that DeepSeek itself had admitted compute shortages limit V4 deployment at scale, with the V4-Pro model unavailable to most API customers in the launch days.

Market reaction

Following the V4 release, shares of SMIC (China's leading chip foundry) rose roughly 9% in Hong Kong trading, with Hua Hong Semiconductor up about 15%, reflecting investor expectations that Huawei Ascend chip demand would increase.[29] Competing Chinese AI startups MiniMax (HKG: 0100) and Knowledge Atlas (Zhipu, HKG: 2513) saw shares fall, with MiniMax sliding around 9-10% in the days after release and Zhipu down 3.4% in Monday trading following V4's launch.[27] The pattern reflected investor rotation out of model developers facing pricing pressure into chip suppliers benefiting from compute demand.

V4's preview release coincided with DeepSeek's first-ever external financing round. According to reporting from Bloomberg, The Information, and CnTechPost, DeepSeek had been in advanced talks since mid-April 2026 to raise at least $300 million at an initial $10 billion target valuation.[23] Within days of the V4 announcement, interest from investors including Tencent and Alibaba pushed the valuation discussion above $20 billion.[24] The financing round marks a sharp pivot for a company that had spent its first two and a half years rejecting venture capital offers in favor of a research-first culture funded by High-Flyer Capital Management.

Developer and commercial adoption

MIT Technology Review identified three reasons V4 matters beyond raw benchmark scores: the compressed attention architecture reduces inference costs to levels that make 1M-token context economically practical for the first time; the open MIT license enables commercial use without licensing negotiations; and the pricing sets a new benchmark for cost pressure on proprietary model providers.[9] Over 90% of developers surveyed by MindStudio included V4-Pro among their top coding model choices in the weeks following release. The legacy API endpoint transition (deepseek-chat to V4-Flash, deepseek-reasoner to V4-Flash thinking mode) signals that V4 is intended as a production replacement, not an experimental release.[7]

V4 was rapidly integrated into agent frameworks and coding tools. The DeepSeek API release notes specifically highlighted compatibility with Claude Code, OpenClaw, and OpenCode, alongside support for both OpenAI ChatCompletions and Anthropic-style API formats.[7] This dual-format support reduced switching costs for developers already using Western model APIs. The July 2026 addition of the Responses API and a published Codex configuration extended the same logic to OpenAI's own client tooling.[33][34]

Reception

Technical coverage was broadly positive but included specific criticisms. Simon Willison, a widely-read developer blogger, described V4 as "almost on the frontier, a fraction of the price," noting that the efficiency improvements at long contexts are genuine and reproducible.[17] He directly quoted DeepSeek's own admission that performance "falls marginally short of GPT-5.4 and Gemini-3.1-Pro, suggesting a developmental trajectory that trails state-of-the-art frontier models by approximately 3 to 6 months."[17] Willison also ran his pelican-on-bicycle SVG generation test, finding that V4-Flash produced a competent rendering while V4-Pro's pelican anatomy was somewhat off.[17]

The Register covered the architecture in depth, describing the KV cache reductions as the most significant technical contribution and emphasizing the MXFP4 adoption as a potential break from Nvidia hardware lock-in.[8] MIT Technology Review framed V4 as the moment when 1M-token context became economically practical for production use.[9] Bloomberg called V4 "DeepSeek's newest flagship a year after [its] AI breakthrough," referencing the V3 and R1 releases of late 2024 and early 2025.[21] CNBC and CNN both ran prominent international coverage on launch day.[11][22]

Developer community reaction on platforms including Reddit (r/DeepSeek, r/singularity, r/LocalLLaMA) and Hugging Face was enthusiastic about the context window, pricing, and Codeforces score. The 3206 Codeforces rating was immediately flagged as a landmark for competitive programming AI.

Criticisms concentrated on a few areas. At launch, some chat instances reportedly identified themselves as V3, suggesting incomplete deployment. Coverage of third-party benchmark results was incomplete in the first week. Developers testing frontend code generation found V4-Pro's output functionally correct but less visually polished than GPT-5.5 on UI tasks. The API initially had reliability problems for V4-Pro under load, with many users encountering rate limits and queuing during peak hours.

The 36Kr report and ChinaTalk analysis added context that V4's development was delayed by training migration failures, talent departures to Tencent, ByteDance, Xiaomi, and other Chinese tech firms, and internal funding decisions that postponed multimodal capability to a later release.[15][30] The 36Kr commentary framed V4 as the "singularity of the Cambrian explosion of AI applications in China," a foundational platform for downstream Chinese AI products rather than a frontier-capability moonshot.[30]

Independent evaluation by Artificial Analysis

Artificial Analysis, which runs its own harness rather than reprinting vendor numbers, gives the clearest outside read on the family. On its Intelligence Index v4.1, an aggregate of nine evaluations including GDPval-AA v2, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, and AA-Omniscience, the firm scored V4-Pro at 44 and the July V4-Flash-0731 build at 50, up from 40 for the April Flash preview.[37][38][39] The smaller model therefore rates six points above the larger one on the index, a gap Artificial Analysis attributed to the newer post-training run rather than to scale.[37]

Where DeepSeek and Artificial Analysis measured the same public benchmark, the numbers diverged. Artificial Analysis measured Terminal-Bench 2.1 for the 0731 Flash build at 79 percent, a 17-point rise over the preview but roughly four points below DeepSeek's reported 82.7, which had been produced with DeepSeek's own unreleased harness at max effort.[7][37] The firm also placed V4-Flash-0731 on its Pareto frontier for intelligence against cost per task and found that, even after an 80 percent price cut on a comparably scoring OpenAI model the day before, running the index through DeepSeek's first-party API cost about 60 percent less.[37]

Two of its findings cut against a simple reading of the gains. The improvement on AA-Omniscience came entirely from a lower hallucination rate, 12 points down to 84 percent, while the share of answers that were correct did not move. And the model is verbose: it consumed roughly 206 million output tokens to complete the index as first reported on July 31 (Artificial Analysis's model page later showed 210 million), down 12 percent from the April build but still far above the cross-model median.[37]

Limitations

Several limitations were noted at launch or in subsequent testing.

Performance gap with closed models. DeepSeek's own technical report states V4 trails state-of-the-art frontier models by approximately three to six months.[5] On MMLU-Pro (87.5% versus Gemini 3.1 Pro at 91.0%) and HLE (37.7% versus Claude Opus 4.6 at 40.0%), the gap to the leading models is measurable. On long-context retrieval (MRCR 1M: 83.5 versus Claude Opus 4.6's 92.9), V4 is competitive but not leading.[2]

No multimodal capability. V4 is text-only at release. Internal funding and compute constraints reportedly pushed multimodal features to a future release.[15] This is a notable gap for teams building agents that require visual validation of outputs, document understanding with figures and tables, or screen-reading workflows. The limitation is enforced at the protocol level: the Responses API declares text as the only input modality and replaces image parts with placeholder text rather than processing them.[33][47]

Complex multi-constraint instruction following. Reviewer testing found that V4-Pro, while strong on structured tasks, shows degraded reliability on complex prompts with many simultaneous constraints, where GPT-5.5 and Claude Opus 4.6 perform more consistently.

Long-horizon agentic reliability. On Terminal-Bench 2.0, V4-Pro scores 67.9% against GPT-5.4 xHigh's 75.1% on the same card, indicating a real gap in multi-step tool-use reliability over extended autonomous task runs.[2]

Scale deployment constraints. CFR analysis notes that compute shortages at DeepSeek itself limit V4 deployment at scale.[14] The full 865 GB V4-Pro model is demanding to self-host, and DeepSeek's own API was reported to be unable to serve V4-Pro to most customers in the days immediately after launch.

IP concerns. Multiple U.S. government officials and analysts have alleged that DeepSeek conducted large-scale distillation from proprietary U.S. frontier models during training. If accurate, this raises questions about the provenance of some capabilities. DeepSeek has not addressed these allegations publicly.

An incomplete official release. As of August 1, 2026 only one of the two models had reached an official build, and that one was still in public beta. The announced official V4-Pro had no date, and Codex and Responses API support for deepseek-v4-pro was still forecast for early August 2026.[6][7][33] The open-weights gap that opened on July 31 closed the same day, so this is now a question of API maturity rather than of access to the checkpoints.[51]

Benchmark reproducibility. DeepSeek's July 2026 agent scores were produced with an unreleased in-house harness, and two of the nine reported benchmarks are internal test sets that no third party can run. The one directly comparable public figure came in about four points lower when Artificial Analysis measured it.[7][37]

Background

How is DeepSeek V4 built? Architecture

Model variants

Hybrid attention: CSA and HCA

Manifold-Constrained Hyper-Connections (mHC)

Muon optimizer

Precision and quantization

Training stability mechanisms

Training methodology

Pre-training

Post-training: specialist-then-distill

DSec: training infrastructure for agents

Tool-call schema with dedicated tokens

Benchmark performance

Coding

Mathematics and science

General knowledge

Long-context

The July 2026 agent-benchmark update

Reading DeepSeek's published scores

How much does DeepSeek V4 cost? Pricing

Official DeepSeek API pricing

Peak and off-peak pricing

Comparison with competing models

Which API formats does DeepSeek V4 support?

Model variants

DeepSeek-V4-Pro

DeepSeek-V4-Flash

Base models

Is DeepSeek V4 open source? Release and licensing

Capabilities

Agentic coding

Long-context comprehension

Reasoning modes

Instruction following and multilingual

Hardware and infrastructure

Training hardware

Huawei Ascend optimization

How does DeepSeek V4 compare with frontier models?

Industry impact

China-US AI competition

Market reaction

Developer and commercial adoption

Reception

Independent evaluation by Artificial Analysis

Limitations

See also

References

Editing: DeepSeek V4