Best Open-Source LLMs

As of July 2026, the strongest open-weight large language model overall is GLM-5.2 from Z.ai (Zhipu AI), which tops the Artificial Analysis open-weight Intelligence Index at 51 and leads all open models on agentic coding, all under a permissive MIT License.^[1][2] Its closest challengers, each scoring 44 on the same index, are DeepSeek V4 (best for math and competitive coding), MiniMax M3, and Kimi K2.6 (best for reasoning and autonomous agents).^[1] If your priority is cheap self-hosting rather than raw intelligence, Google DeepMind's Gemma 4 and Alibaba's Qwen3.5, both under Apache-2.0, are the best small-footprint picks.^[3][4]

This is a ranked, benchmark-anchored guide to the best open-weight LLMs by use case. Every figure is dated to July 2026 and cited to a primary source or a reputable benchmark leaderboard. Rankings use the Artificial Analysis Intelligence Index (v4.1), an independent composite that weights agentic, coding, scientific, and general reasoning evaluations; individual benchmark numbers are noted as vendor-reported where they come from a model card rather than an independent harness.

Best open-source LLM by use case: the quick verdict

Scroll sideways for more →

Last verified: July 2026. Access for every model below is open-weight (downloadable from Hugging Face).

1. GLM-5.2: best all-around open-weight model

GLM-5.2, released by Z.ai (the international brand of Beijing-based Zhipu AI) on June 13, 2026 with weights published June 16, is the highest-scoring open-weight model on the Artificial Analysis Intelligence Index at 51, ahead of every other open release and closing on the strongest proprietary systems.^[1][2] It is a mixture-of-experts model of roughly 753 billion total parameters with about 40 billion active per token, ships a 1M-token context window, and is distributed under a pure MIT License.^[2][8] It is the direct successor to GLM-5.1 and the GLM-5 base, the series Z.ai trained on Huawei Ascend accelerators rather than Nvidia hardware.^[9]

Its defining strength is long-horizon agentic engineering. GLM-5.2 leads open models on SWE-Bench Pro with 62.1%, beating GPT-5.5 (58.6%), and scores 81.0 on Terminal-Bench 2.1 and 74.4 on FrontierSWE, at roughly one-sixth of GPT-5.5's cost per token.^[8][10] Best-for: teams that want a single self-hostable model for chat, tool use, and multi-hour coding agents. Minimum hardware: this is a frontier-scale model, so full weights need a multi-GPU server (for example an 8x H100-class node); community 4-bit GGUF builds of roughly 400 GB bring it within reach of high-end workstations.^[8]

2. DeepSeek V4: best for coding and competitive programming

DeepSeek V4, previewed by DeepSeek on April 24, 2026, ships in two open-weight variants under the MIT License: V4-Pro (1.6 trillion total, 49 billion active) and V4-Flash (284 billion total, 13 billion active), both with a 1M-token context.^[5] V4-Pro scores 44 on the Intelligence Index and V4-Flash 40.^[1] On coding it is exceptional: SWE-bench Verified 80.6%, LiveCodeBench 93.5 for the Pro-Max configuration, and a Codeforces rating of 3206, the highest competitive-programming score any AI model had reached at release.^[5] It also posts MMLU-Pro 87.5%, GPQA Diamond 90.1%, AIME 2026 96.4%, and a perfect score on Putnam 2025.^[5]

Best-for: repository-scale software work and hard math where you also want an MIT license and cheap long-context inference. Minimum hardware: V4-Flash is the accessible option at a 160 GB checkpoint (roughly two H100-class GPUs at FP8), while V4-Pro's 862 GB checkpoint needs a full 8-GPU node.^[5] DeepSeek's own report notes V4 still trails the very top proprietary frontier models by about 3 to 6 months.^[5]

3. Kimi K2.6: best for reasoning and autonomous agents

Kimi K2.6 from Moonshot AI, released April 20, 2026, is a roughly 1-trillion-parameter MoE (about 32 billion active) with native INT4 quantization, a 256K context, and a Modified MIT License.^[1][6] It scores 44 on the current Intelligence Index; at launch it was the top open model at 54 on the previous (v4.0) index.^[1][6] On reasoning it leads the open field with GPQA Diamond 90.5, AIME 2026 96.4, SWE-bench Verified 80.2, and Humanity's Last Exam 54.0 with tools.^[6] Unlike the earlier text-only Kimi K2, K2.6 is natively multimodal and adds Agent Swarm, which fans a single request out across as many as 300 parallel sub-agents running up to 4,000 coordinated steps.^[6]

Best-for: agentic pipelines and multi-agent orchestration where a configurable thinking mode and vision input matter. Minimum hardware: even at native INT4 the trillion-parameter weights demand a multi-GPU node; Moonshot recommends serving with vLLM, SGLang, or KTransformers.^[6]

4. Qwen3.5: best for long context on a budget

Qwen3.5, released by Alibaba's Qwen team on February 16, 2026, is an Apache-2.0 family whose flagship Qwen3.5-397B-A17B pairs a 397-billion-parameter sparse MoE (17 billion active) with linear attention via Gated DeltaNet.^[4] That hybrid stack is what makes long context cheap: most layers run at constant per-token memory, so the model holds a native 262,144-token (256K) window and extends toward roughly 1M tokens with YaRN scaling, with the hosted Qwen3.5-Plus endpoint exposing 1M by default.^[4] It scores 34 on the Intelligence Index and reports GPQA 88.4, AIME 2026 91.3, and SWE-bench Verified 76.4.^[1][4] Every one of its eight checkpoints, from 397B down to sub-billion, is natively multimodal and covers 201 languages.^[4]

Best-for: long-document and multilingual work where you want a permissive license and want to scale down to a single GPU. Note that the separate Qwen3-Max tier is closed-weight, so Qwen3.5 is Alibaba's open flagship.^[4] Minimum hardware: the 397B flagship needs multi-GPU serving, but the 35B-A3B and smaller family members run on one GPU or a laptop.^[4]

5. Mistral Large 3: best fully permissive (Apache-2.0) flagship

Mistral Large 3, released by Mistral AI on December 2, 2025, is the largest Apache-2.0 MoE flagship from a major lab: roughly 675 billion total parameters, about 41 billion active, a 256K context, and integrated image understanding.^[7] Both base and instruction-tuned checkpoints ship under Apache-2.0 with no usage restrictions, which is why it is the pick when license cleanliness is the priority.^[7] It is a non-reasoning generalist, so it is strong on knowledge and multilingual chat (LMArena Elo about 1418, second among open non-reasoning models at launch, and MMLU-Pro around 73.1) but weaker on reasoning-heavy tests such as GPQA Diamond at about 43.9.^[7] API pricing is $0.50 per 1M input and $1.50 per 1M output tokens.^[7]

Best-for: European-hosted, fully unrestricted commercial deployment and multilingual generalist tasks. Minimum hardware: the FP8 instruct checkpoint fits a single 8-GPU H200 or B200 node, an NVFP4 build serves on H100 or A100 nodes (best below 64K context), and the smaller Ministral 3 models (3B, 8B, 14B, also Apache-2.0) run on a laptop or edge device.^[7]

6. Gemma 4: best to self-host cheaply

Gemma 4, released by Google DeepMind on April 2, 2026 and built from Gemini 3 research, is the first Gemma generation under Apache-2.0 rather than Google's custom terms, and it is the easiest serious model to run locally.^[3] The family spans two edge models (E2B and E4B), a 26B mixture-of-experts model with about 3.8 billion active parameters, and a 31B dense flagship, with 256K context on the larger pair.^[3] The 31B model scores 29 on the Intelligence Index yet posts MMLU-Pro 85.2, GPQA Diamond 84.3, AIME 2026 89.2 without tools, LiveCodeBench v6 80.0, and an LMArena Elo of about 1452, third among open models at launch and above models many times its size.^[3]

Best-for: on-device agents, offline use, and anyone GPU-constrained. Minimum hardware: E2B can run in under 1.5 GB of memory on a phone or single-board computer, and the 31B model fits on a single accelerator; day-one runtimes include Ollama, vLLM, llama.cpp, and MLX for Apple Silicon.^[3]

7. Llama 4: the aging incumbent with the biggest ecosystem

Llama 4 from Meta, released April 5, 2025, remains one of the most widely deployed open-weight families, but it has fallen well behind the 2026 frontier.^[11] Llama 4 Scout (109 billion total, 17 billion active) advertises a 10M-token context and fits on a single H100 at INT4, while Llama 4 Maverick (about 400 billion total, 17 billion active) supports 1M tokens and fits an 8-GPU node.^[11] Maverick's Meta-reported April 2025 numbers were MMLU-Pro 80.5 and GPQA Diamond 69.8, but independent aggregators later put its Intelligence Index near 18, and its launch was marred by a Chatbot Arena benchmark-manipulation controversy.^[11] Scout's 10M context is a needle-in-a-haystack figure; effective long-form usable context is substantially lower in practice.^[11]

The bigger caveat is strategic: Meta ended the open-weight Llama line and launched its closed successor Muse Spark on April 8, 2026, so Llama 4 is the last open Llama and Llama 4 Behemoth was never released.^[11] It is distributed under the Llama 4 Community License, which is source-available with acceptable-use and large-platform restrictions rather than an OSI-approved open-source license.^[11] Best-for: teams already invested in the Llama tooling ecosystem that need a battle-tested, deployable model rather than the top score.

Other open models worth knowing

Three more open releases place well on the July 2026 leaderboard. MiniMax M3 ties DeepSeek V4 Pro and Kimi K2.6 at Intelligence Index 44 with a 1M context.^[1] NVIDIA's Nemotron 3 Ultra 550B scores 38, and OpenAI's gpt-oss 120B, released under Apache-2.0, scores 24 and is a common lightweight local default.^[1] Xiaomi's MiMo-V2.5-Pro also registers a strong 42.^[1]

Summary comparison table

Open-weight LLMs ranked by Artificial Analysis Intelligence Index. Benchmarks are as-of July 2026; blank cells are not reported (n/r). Vendor-reported figures reflect each publisher's own evaluation setup.

Scroll sideways for more →

Chatbot Arena Elo is not reported by most of this cohort; where available it is about 1452 for Gemma 4 31B, 1418 for Mistral Large 3, and a disputed 1417 for the experimental Llama 4 Maverick entry.^[3][7][11]

Which open-source LLM should you choose?

Which is best for coding? GLM-5.2 for long-horizon agentic engineering (SWE-Bench Pro 62.1) and DeepSeek V4 for competitive programming and repository-scale tasks (Codeforces 3206, SWE-bench Verified 80.6).^[5][8] Which is best for reasoning and math? Kimi K2.6 and DeepSeek V4 are level, both at AIME 2026 96.4, with Kimi edging GPQA Diamond at 90.5.^[5][6] Which has the biggest usable context? DeepSeek V4, GLM-5.2, and MiniMax M3 all offer a genuine 1M-token window; Qwen3.5 reaches similar lengths most cheaply through linear attention.^[1][4][5] Which is cheapest to self-host? Gemma 4, whose E2B edge model runs in under 1.5 GB and whose 31B fits a single GPU.^[3] Which has the most permissive license? Any of the Apache-2.0 or MIT models: Mistral Large 3, Qwen3.5, and Gemma 4 are Apache-2.0, while GLM-5.2, DeepSeek V4, and Kimi K2.6 are MIT or Modified MIT.^{[2][3][4][5][6][7]} Avoid Llama 4 if you need a current top model or an OSI-approved license.^[11]

How these were ranked

Overall standing follows the Artificial Analysis Intelligence Index (v4.1), an independent composite over agentic, coding, scientific, and general reasoning evaluations, captured in July 2026; note that Artificial Analysis rescaled its index during 2026, so a model's launch-day score (for example Kimi K2.6 at 54) is not directly comparable with the current v4.1 value (44).^[1][6] Per-task benchmarks (MMLU-Pro, GPQA Diamond, SWE-bench Verified and Pro, AIME) come from model cards and launch reports and are treated as vendor-reported unless an independent harness confirmed them; blank cells are left as not reported rather than estimated.^[1] License names use their canonical form and link to the corresponding license page.

References

1. Artificial Analysis, "Comparison of Open Source AI Models across Intelligence, Performance, Price and Context" (Intelligence Index, accessed July 2026). https://artificialanalysis.ai/models/open-source ↩
2. llm-stats.com, "GLM-5.2 Benchmarks, Pricing & Size" (accessed July 2026). https://llm-stats.com/models/glm-5.2 ↩
3. Google Open Source Blog, "Gemma 4: Expanding the Gemmaverse with Apache 2.0," April 2, 2026. https://opensource.googleblog.com/2026/03/gemma-4-expanding-the-gemmaverse-with-apache-20.html ↩
4. MarkTechPost, "Alibaba Qwen Team Releases Qwen3.5-397B MoE Model with 17B Active Parameters and 1M Token Context," February 16, 2026. https://www.marktechpost.com/2026/02/16/alibaba-qwen-team-releases-qwen3-5-397b-moe-model-with-17b-active-parameters-and-1m-token-context-for-ai-agents/ ↩
5. DeepSeek-AI, "DeepSeek-V4 Technical Report" and V4 model card, Hugging Face, April 2026 (figures summarized in AI Wiki, "DeepSeek V4," https://aiwiki.ai/wiki/deepseek_v4). ↩
6. Moonshot AI, "Kimi K2.6" model card, Hugging Face, April 20, 2026; Artificial Analysis K2.6 analysis, April 21, 2026 (summarized in AI Wiki, "Kimi K2.6," https://aiwiki.ai/wiki/kimi_k2_6). ↩
7. Mistral AI, "Introducing Mistral 3," December 2, 2025. https://mistral.ai/news/mistral-3/ ↩
8. DataCamp, "GLM-5.2: Features, Setup, Benchmarks, and Model Switching Guide," June 2026. https://www.datacamp.com/blog/glm-5-2 ↩
9. MarkTechPost, "Z.AI Introduces GLM-5.1: An Open-Weight 754B Agentic Model," April 8, 2026. https://www.marktechpost.com/2026/04/08/z-ai-introduces-glm-5-1-an-open-weight-754b-agentic-model-that-achieves-sota-on-swe-bench-pro-and-sustains-8-hour-autonomous-execution/ ↩
10. VentureBeat, "Z.ai's open-weights GLM-5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for 1/6th the cost," June 2026. https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost ↩
11. Meta AI, "The Llama 4 herd," April 5, 2025 (later benchmark and strategy context summarized in AI Wiki, "Llama 4," https://aiwiki.ai/wiki/llama_4). ↩