# GPT-5.6

> Source: https://aiwiki.ai/wiki/gpt_5_6
> Updated: 2026-09-04
> Fact-checked: 2026-09-04
> Categories: AI Models, Large Language Models, OpenAI, Reasoning Models
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "GPT-5.6." aiwiki.ai, 4 Sept 2026. https://aiwiki.ai/wiki/gpt_5_6
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

GPT-5.6 is a family of proprietary multimodal [large language models](https://aiwiki.ai/wiki/large_language_model) developed by [OpenAI](https://aiwiki.ai/wiki/openai). The family entered a limited preview on June 26, 2026, and became generally available on July 9, 2026. It has three models: GPT-5.6 Sol, the flagship tier; GPT-5.6 Terra, a lower-cost model intended to balance capability and price; and GPT-5.6 Luna, the fastest and least expensive tier.[1][2] Three weeks after general availability, on July 30, 2026, OpenAI cut Luna's API price by 80 percent and Terra's by 20 percent while leaving Sol's rates unchanged.[28] Sol followed on August 21, when OpenAI introduced promotional rates of $4 per million input tokens and $20 per million output tokens, valid at least through November 21, 2026.[41]

On August 13, OpenAI began a limited API preview of an additional Cerebras-backed service tier for Sol called Ultrafast. It was restricted to selected customers and advertised at up to 750 output tokens per second.[38]

On September 3, 2026, OpenAI released [GPT-6 Astra](https://aiwiki.ai/wiki/gpt_6_astra) as the next generation. It shipped as a single model plus an Astra Pro configuration rather than as a Sol, Terra, and Luna set; OpenAI's launch post compares it with GPT-5.6 Sol throughout, and that comparison is summarised at the end of this article.[43][44]

The release is notable for two reasons beyond the models themselves. It replaced OpenAI's size suffixes with named capability tiers, and its preview was restricted at the request of the United States government to a small set of pre-approved organizations, which Axios described as the first time the US government had preemptively asked an American AI company to restrict a model launch before release.[1][11][12]

OpenAI introduced Sol, Terra, and Luna as durable capability tiers that can advance on separate schedules. This replaced the earlier pattern of using unsuffixed, mini, and nano labels for different sizes within one GPT generation. The models are designed for [reasoning](https://aiwiki.ai/wiki/reasoning_models), coding, professional knowledge work, scientific research, [computer use](https://aiwiki.ai/wiki/computer_use), and tool-based workflows.[2][7]

## Release and naming

The June 26 preview was limited to selected partners and organizations. Participants could receive access through the API, [Codex](https://aiwiki.ai/wiki/openai_codex), or both, while ordinary [ChatGPT](https://aiwiki.ai/wiki/chatgpt) users did not have access. OpenAI said it used the preview period for additional testing and coordination before a broader release, and that it worked with expert organizations and trusted partners to pressure-test the safeguards.[1][2]

OpenAI confirmed the general availability date late on July 7, setting the launch for 10am Pacific on July 9; its developer forum thread carries the same time.[12][25] On July 9, it announced general availability across ChatGPT, Codex, and the OpenAI API, describing the rollout as global but gradual over the following 24 hours. The number in GPT-5.6 identifies the model generation, while Sol, Terra, and Luna identify capability and price tiers. The API alias `gpt-5.6` routes to `gpt-5.6-sol`.[2][7]

OpenAI's documentation maps the new names onto the old ones: Sol "roughly corresponds to the unsuffixed model tier used in earlier GPT-5 families", Terra to the mini tier, and Luna to the nano tier.[4][5][6]

| Model | API model ID | Intended role | Context window | Maximum output | Knowledge cutoff |
| --- | --- | --- | ---: | ---: | --- |
| GPT-5.6 Sol | `gpt-5.6-sol` | Flagship model for complex professional work | 1,050,000 tokens | 128,000 tokens | February 16, 2026 |
| GPT-5.6 Terra | `gpt-5.6-terra` | Balanced capability and cost | 1,050,000 tokens | 128,000 tokens | February 16, 2026 |
| GPT-5.6 Luna | `gpt-5.6-luna` | Cost-sensitive, high-volume work | 1,050,000 tokens | 128,000 tokens | February 16, 2026 |

OpenAI's model documentation lists the same [context window](https://aiwiki.ai/wiki/context_window), maximum output, and knowledge cutoff for all three models. The 1,050,000-token figure is usually described in press coverage as a one-million-token window. Each model accepts text and image input and returns text. Audio and video input are not supported, and none of the three models supports fine-tuning.[4][5][6][18]

Snapshot handling is unusual for an OpenAI launch: as of late July 2026 each model listed only a single snapshot identical to its alias, so `gpt-5.6-sol` was both the alias and the only pinned version available.[4][5][6]

## The government review

On June 25, 2026, Axios reported that the Trump administration had asked OpenAI to restrict the release of GPT-5.6 to a small set of government-approved partners before any wider launch, citing security concerns. The request came from the White House Office of the National Cyber Director and the Office of Science and Technology Policy while the administration was building a framework for testing new models. Axios described it as the first time the US government had preemptively asked an American AI company to restrict a launch. According to The Information, [Sam Altman](https://aiwiki.ai/wiki/sam_altman) told employees in a memo that "we've made clear to the U.S. government that this is not our preferred long term model", and said he hoped to release GPT-5.6 a couple of weeks later.[11]

OpenAI's own preview post confirmed the arrangement without naming the agencies involved. It said that "as part of our ongoing engagement with the U.S. government, we previewed our plans and the models' capabilities ahead of today's launch", that the limited preview covered "a small group of trusted partners whose participation has been shared with the government", and that "we don't believe this kind of government access process should become the long-term default". OpenAI framed the step as short-term, taken while it worked with the administration on a cyber executive order framework and a repeatable process for future model releases.[1]

The preview lasted 13 days. On July 8, Axios reported that the administration had cleared a broad launch after additional testing by the Center for AI Standards and Innovation, the Commerce Department body that succeeded the [US AI Safety Institute](https://aiwiki.ai/wiki/us_aisi), with OpenAI sending technical staff to Washington to answer questions. A White House official disputed the framing, telling Axios that "no such permission is required or granted" and that decisions on release timing "rest entirely with the companies", pointing to President Trump's [June 2 AI executive order](https://aiwiki.ai/wiki/ai_executive_order), which bars mandatory federal licensing or preclearance for model releases and makes government testing voluntary.[12]

Context matters for how unusual this was. The same Commerce Department had in June 2026 barred foreign access to Anthropic's Mythos and Fable models, effectively pulling them from the market, with the restriction on Fable lifted at the end of that month. An Axios source attributed the GPT-5.6 intervention to the model having "Mythos-like" capability rather than to a general shift toward heavier regulation.[11][12]

OpenAI's system card does not mention the Center for AI Standards and Innovation, the Commerce Department, or the executive order. The government review is documented only in press reporting and in OpenAI's general references to engagement with the US government.[1][3]

## API features and pricing

All three models support the Responses and Chat Completions APIs, streaming, function calling, structured outputs, and reasoning tokens. Through the Responses API they can use hosted tools including web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, [Model Context Protocol](https://aiwiki.ai/wiki/model_context_protocol) connections, and tool search.[4][5][6]

GPT-5.6 supports six reasoning-effort settings in the API: `none`, `low`, `medium`, `high`, `xhigh`, and `max`. The default is `medium`. Pro is a request mode selected with `reasoning.mode: "pro"`, not a separate model ID, and it works with any GPT-5.6 model at any effort level; OpenAI advises against switching to a separate Pro model slug. The company's migration guidance tells developers to keep their existing GPT-5.5 or GPT-5.4 effort level as a baseline and to test one level lower, on the theory that GPT-5.6 can hold quality with fewer tokens.[7]

Five capabilities were new or changed at launch.[2][7]

| Feature | What it does | Status at launch |
| --- | --- | --- |
| Programmatic Tool Calling | The model writes JavaScript that calls eligible tools in a hosted runtime, passes results between calls, and reduces large intermediate outputs before returning them | Generally available in the Responses API; supports Zero Data Retention workflows without a persistent code-execution container |
| Multi-agent | One GPT-5.6 instance coordinates parallel subagents and synthesizes their results in a single request | Beta in the Responses API |
| Explicit [prompt caching](https://aiwiki.ai/wiki/prompt_caching) | Developers mark which reusable prefixes are cached using breakpoints or `prompt_cache_options.mode: "explicit"`, with a fixed 30-minute cache TTL, currently the only supported value; automatic caching still works | Generally available |
| Persisted reasoning | `reasoning.context` set to `all_turns` or `current_turn` controls whether reasoning items carry across turns | Generally available |
| Original image detail | `detail: original` preserves an image's dimensions instead of resizing it to a patch budget | Generally available |

Programmatic Tool Calling is aimed at bounded, tool-heavy work such as filtering, joining, ranking, deduplication, and aggregation, where code can compress several tool results into a small structured answer. OpenAI's guidance is that a single lookup or action does not justify it, and that direct calls remain preferable when each result should change the model's next decision, when a write or other action needs approval, and when citations or native artifacts have to be preserved. Chains of dependent calls are a case for the feature rather than against it, provided the data flow is predictable.[7][23]

Launch prices were stated per 1 million text tokens. These are the rates that applied from July 9 until the price change of July 30, 2026 described below; Sol's held until the promotional cut of August 21, 2026:[2][4][5][6]

| Model | Input | Cached input | Output |
| --- | ---: | ---: | ---: |
| GPT-5.6 Sol | $5.00 | $0.50 | $30.00 |
| GPT-5.6 Terra | $2.50 | $0.25 | $15.00 |
| GPT-5.6 Luna | $1.00 | $0.10 | $6.00 |

For requests containing more than 272,000 input tokens, OpenAI charges twice the normal input rate and 1.5 times the normal output rate for the full request. Cache writes cost 1.25 times the uncached input rate, while cache reads retain a 90 percent discount.[2][24] The Batch API halves both headline rates, so at launch a Sol batch job was billed at $2.50 per million input tokens and $15.00 per million output tokens, Terra at $1.25 and $7.50, and Luna at $0.50 and $3.00.[26] [Artificial Analysis](https://aiwiki.ai/wiki/artificial_analysis) noted that cache-write pricing was a first for OpenAI and brought it in line with Anthropic's practice; the same article observed that the `max` effort level also matched a control Anthropic already offered.[2][4][5][6][9]

Rate limits differ sharply across the family. At usage tier 5, Sol and Terra are capped at 15,000 requests and 40 million tokens per minute, while Luna reaches 30,000 requests and 180 million tokens per minute, which is consistent with its positioning for high-volume work. None of the three is available on the free API tier.[4][5][6]

Developer Simon Willison, who had early access, priced the same SVG-drawing prompt across all three models and six effort levels: the cheapest run was Luna at effort `none` for 0.71 cents, and the most expensive was Sol at `max` for 48.55 cents. Both figures were computed on the launch prices, before the July 30 cut. His point was that per-token prices no longer describe cost well when reasoning-token counts vary by two orders of magnitude for the same task.[18][13]

### The July 30 price cut

On July 30, 2026, three weeks after general availability, OpenAI reduced the API price of the two cheaper tiers. The changelog entry for that day reads: "Starting July 30, GPT-5.6 Luna costs 80% less, while GPT-5.6 Terra costs 20% less." Sol's rates were untouched.[28][29]

| Model | Input, launch | Input, from July 30 | Output, launch | Output, from July 30 | Change |
| --- | ---: | ---: | ---: | ---: | --- |
| GPT-5.6 Sol | $5.00 | $5.00 | $30.00 | $30.00 | none |
| GPT-5.6 Terra | $2.50 | $2.00 | $15.00 | $12.00 | 20 percent lower |
| GPT-5.6 Luna | $1.00 | $0.20 | $6.00 | $1.20 | 80 percent lower |

Cached input moved with the headline rates, from $0.25 to $0.20 for Terra and from $0.10 to $0.02 for Luna, so the 90 percent cache-read discount survived the change. The rest of the rate structure was unchanged. The long-context rule still charges 2 times input and 1.5 times output on any request above 272,000 input tokens, which puts Luna's long-context rates at $0.40 input, $0.04 cached input and $1.80 output, and Terra's at $4.00, $0.40 and $18.00. Cache writes are still billed at 1.25 times the uncached input rate, $0.25 per million tokens for Luna in short context. Batch and Flex, both half the standard rate, fell with it: Luna now costs $0.10 input, $0.01 cached and $0.60 output in short context, and Terra $1.00, $0.10 and $6.00. Rate limits were not part of the announcement, and the model pages still list Luna at 30,000 requests and 180 million tokens per minute at usage tier 5 against Terra's 15,000 and 40 million.[5][6][28][29]

The same changelog entry introduced Fast mode, which replaces the older Priority Processing option. For Sol, OpenAI said Fast mode runs up to 2.5 times faster than standard processing at twice the standard price. The change is backward compatible: requests already tagged `priority` are routed to Fast mode, and the pricing page notes that "Priority processing was renamed Fast mode on July 30, 2026" and that either `service_tier: "priority"` or `service_tier: "fast"` works. Fast mode is priced at exactly double the standard rate for all three models, so at the rates then in force Sol cost $10.00 input and $60.00 output, Terra $4.00 and $24.00, and Luna $0.40 and $2.40; Sol's Fast rate fell to $8.00 and $40.00 with the August 21 cut described below.[28][29][39][42]

### The August 13 Ultrafast preview

OpenAI began a limited preview of GPT-5.6 Sol Ultrafast on August 13, 2026. Ultrafast is a processing service tier for Sol, not a fourth GPT-5.6 model or a new checkpoint. It is powered by Cerebras and launched first in the OpenAI API, but only for a selected group of customers. OpenAI said access would expand as capacity grew and provided a form for access updates.[38]

| Processing option | Access on August 14, 2026 | Selection and pricing | Published performance statement |
| --- | --- | --- | --- |
| Standard Sol | Generally available in the API | Model `gpt-5.6-sol`; standard token prices | Reference tier |
| Fast Sol | Pay-as-you-go API processing | `service_tier: "fast"` or the legacy value `priority`; twice Standard prices | Up to 2.5 times faster than Standard; enterprise-only Sol latency SLA listed as 99 percent above 80 tokens per second |
| Ultrafast Sol | Limited preview for selected customers | Public selector and price not disclosed in the announcement | Up to 14 times faster than Standard and up to 750 output tokens per second |

The Fast-mode SLA is calculated as p50 request latency on a per-five-minute basis and applies only to enterprise customers. Fast also has a documented ramp-rate rule under which rapidly increasing traffic can be sent to Standard processing. OpenAI says Fast supports the same multimodal capabilities as Standard. The Ultrafast announcement does not state that these Fast-mode terms carry over, and it does not publish Ultrafast pricing, a public model ID or service-tier value, rate limits, an SLA, context limits, feature parity, or a general-availability date.[38][39]

OpenAI describes both Ultrafast figures as "up to" values. Its announcement does not disclose a test suite, workload distribution, output length, reasoning effort, batch size, load level, repeat count, time to first token, or percentile distribution for either claim. The 750 figure is specifically an output-token rate; the announcement does not report end-to-end request latency. OpenAI also shows Standard and Ultrafast building a 3D warehouse simulator side by side, but gives no timing or reproducible protocol for that example. Testimonials from Jane Street, Podium, Basis, and Rogo describe early uses but are not controlled comparisons.[38]

Cerebras published one same-prompt demonstration in which Sol built a financial terminal-style dashboard in 1 minute 50 seconds on Ultrafast and 12 minutes 20 seconds on Standard. The elapsed-time ratio is approximately 6.73. This single vendor demonstration measures an end-to-end task, not the same quantity as output tokens per second. Cerebras did not publish the complete prompt, reasoning and tool settings, output-token counts, repetition procedure, or an independent quality assessment; its statement that the result was the same is a vendor characterization.[40]

The similarly named `ultra` product setting described below coordinates subagents in Codex and ChatGPT Work. It is separate from the Ultrafast inference service tier.[2][22][38]

OpenAI attributed the cut to its own serving costs rather than to the market. It said the efficiency gains came out of building GPT-5.6, including the model rewriting and optimising production code, and that they reduced the end-to-end cost of serving the model by 20 percent and improved token-generation efficiency by more than 15 percent. The announcement thread in OpenAI's developer forum attributes the first figure to production GPU kernel improvements and the second to better [speculative decoding](https://aiwiki.ai/wiki/speculative_decoding), and adds that OpenAI was moving Auto-review in the ChatGPT app and the Codex CLI from GPT-5.4 to GPT-5.6 Luna, which it expected to cost about ten times less.[30][34]

The cut moved Luna into the price band occupied by the cheapest Chinese frontier models without quite reaching it. DeepSeek lists [DeepSeek V4-Flash](https://aiwiki.ai/wiki/deepseek_v4_flash) at $0.14 per million input tokens on a cache miss and $0.28 per million output tokens, with cache hits at $0.0028.[33]

| Model | Input (cache miss) | Cached or cache-hit input | Output |
| --- | ---: | ---: | ---: |
| GPT-5.6 Luna, from July 30, 2026 | $0.20 | $0.02 | $1.20 |
| DeepSeek V4-Flash | $0.14 | $0.0028 | $0.28 |

Both rows are per 1 million tokens on the vendor's own API at the standard tier, and the Luna row is its short-context band.[29][33]

Artificial Analysis, publishing on July 31, scored the DeepSeek V4 Flash 0731 release at 50 on its Intelligence Index against 51 for Luna at maximum reasoning, and reported that "even after OpenAI's 80% price cut on GPT-5.6 Luna", the DeepSeek model's cost per task on DeepSeek's first-party API "comes in at ~60% lower than GPT-5.6 Luna (max)".[32]

### The August 21 Sol promotional price

On August 21, 2026, OpenAI cut Sol's Standard API price to $4 per million input tokens and $20 per million output tokens. The changelog entry describes this as "20% lower input pricing and 33% lower output pricing" and adds that "GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026"; the Sol model page carries the same sentence. The changelog entry gives no reason for the cut and does not state a post-promotion rate.[41][4]

| Model | Input | Cached input | Output | Status on September 3, 2026 |
| --- | ---: | ---: | ---: | --- |
| GPT-5.6 Sol | $4.00 | $0.40 | $20.00 | Promotional rate since August 21, at least through November 21, 2026 |
| GPT-5.6 Terra | $2.00 | $0.20 | $12.00 | Unchanged since July 30 |
| GPT-5.6 Luna | $0.20 | $0.02 | $1.20 | Unchanged since July 30 |

The rest of Sol's rate structure moved with the headline numbers. Cache writes are still billed at 1.25 times the uncached input rate, now $5.00 per million tokens; the long-context multipliers of 2 times input and 1.5 times output still apply above 272,000 input tokens; Fast mode is still twice Standard, which puts Fast Sol at $8.00 input and $40.00 output; and Batch and Flex are half of Standard at $2.00 and $10.00. Terra and Luna were not part of the change.[42]

The cut came 13 days before GPT-6 Astra launched at $10 and $50 per million tokens, so the successor lists at exactly 2.5 times Sol's promotional rate on both input and output.[41][43][44]

## Product access

At general availability, standard ChatGPT conversations offered GPT-5.6 Sol to eligible paid plans through the Medium, High, and Extra High reasoning options, with the Pro option powered by a separate GPT-5.6 Sol Pro configuration. GPT-5.5 Instant remained the default for fast everyday responses, and Terra and Luna were not selectable in standard chats.[8]

| ChatGPT plan | Medium and High | Extra High | Pro |
| --- | --- | --- | --- |
| Free and Go | Not included | Not included | Not included |
| Plus | Included | Not included | Not included |
| Pro | Included | Included | Included |
| Business | Included | Included | Included |
| Enterprise | Included | Included | Included |

Logged-out users have no access to Sol at all. Work in ChatGPT exposed all three models to Plus, Pro, Business, and Enterprise users, and Codex offered Terra to Free and Go users and all three models to paid tiers. The OpenAI API exposed Sol, Terra, and Luna to developers. OpenAI described the rollout as global but gradual, so account-level availability could lag the July 9 announcement.[2][8]

GPT-5.6 does not carry its own country list; access follows the existing ChatGPT and API country lists. It was available to eligible users in the EEA, Switzerland, the United Kingdom, and the UAE, though it was not supported for workloads configured to use UAE inference residency.[8]

The launch coincided with a broader product reshuffle. OpenAI shipped ChatGPT Work, an agent for longer multistep tasks, and folded Codex, chat, and Work into a single desktop application. Axios described the result as a "jumble of new model names, reasoning levels and subscription tiers" that users had to sort out for themselves, and noted that free and Go subscribers get Terra only inside Work and Codex, not in ordinary chat.[13]

### The ultra setting

Alongside the API effort levels, OpenAI added a product-level mode called `ultra`. It is exposed as a sixth step in the Codex and ChatGPT Work reasoning picker, described there as "maximum reasoning with automatic task delegation", and it works by delegating to subagents rather than by thinking longer in a single run. OpenAI's launch post says ultra "goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result", and its published charts also show 16-agent configurations on two benchmarks.[2][22]

In ChatGPT Work, ultra is limited to Pro and Enterprise subscribers; in Codex, it is available from Plus upward. There is no `ultra` value for the API's `reasoning.effort` parameter. Developers who want the same behaviour use the multi-agent beta in the Responses API, which OpenAI describes as similar to ultra mode in Codex.[2][7]

### Third-party distribution

OpenAI announced on launch day, in a post carrying a quote from Microsoft's Copilot and Agents Core president Nitin Agrawal, that GPT-5.6 would become the preferred model in [Microsoft 365 Copilot](https://aiwiki.ai/wiki/microsoft_365_copilot) across Word, Excel, PowerPoint, Chat, and Cowork, served both natively and through the OpenAI API. In its June preview, OpenAI separately said it expected to offer GPT-5.6 Sol on [Cerebras](https://aiwiki.ai/wiki/cerebras) hardware at up to 750 tokens per second during July, initially for selected customers. The named deployment entered limited preview on August 13 as the Ultrafast API service tier, after that stated July window, and access remained restricted to selected customers.[1][21][38]

## Evaluation

OpenAI reported results across professional work, coding, science, computer use, cybersecurity, long-context retrieval, and academic reasoning. The following selection uses OpenAI's published launch configurations. Scores depend on the harness, reasoning setting, tool access, and token budget, so they are not direct measures of performance in every application. The figures are as published by OpenAI on launch day; the two Artificial Analysis index rows are third-party measurements that OpenAI reproduced rather than ran itself.[2]

| Evaluation | Sol | Terra | Luna | GPT-5.5 |
| --- | ---: | ---: | ---: | ---: |
| Agents' Last Exam | 52.7% | 50.4% | 50.3% | 46.9% |
| GDPval-AA v2 | 1,747.8 Elo | 1,593 Elo | 1,591.8 Elo | 1,493.7 Elo |
| Artificial Analysis Intelligence Index v4.1 | 58.9 | 55 | 51.2 | 54.8 |
| Artificial Analysis Coding Agent Index v1.1 | 80 | 77.4 | 74.6 | 76.4 |
| [SWE-Bench Pro](https://aiwiki.ai/wiki/swe_bench_pro) | 64.6% | 63.4% | 62.7% | 59.4% |
| DeepSWE v1.1 | 72.7% | 69.6% | 67.2% | 67% |
| [Terminal-Bench 2.1](https://aiwiki.ai/wiki/terminal_bench) | 88.8% | 87.4% | 84.7% | 85.6% |
| [OSWorld 2.0](https://aiwiki.ai/wiki/osworld) | 62.6% | 50.2% | 45.6% | 47.5% |
| [BrowseComp](https://aiwiki.ai/wiki/browsecomp) | 90.4% | 87.5% | 83.3% | 84.4% |
| Capture-the-Flag Challenges | 96.7% | 91.8% | 85.2% | 88.1% |
| SEC-Bench Pro | 71.2% | 57.7% | 48.9% | 45.8% |
| ExploitBench | 73.5% | 52.9% | 33.2% | 47.9% |
| ExploitGym | 33.7% | 23.2% | 12.4% | 15.1% |
| [GPQA Diamond](https://aiwiki.ai/wiki/gpqa) | 94.6% | 92.9% | 92.3% | 93.6% |
| [FrontierMath](https://aiwiki.ai/wiki/frontiermath) Tier 1-3 (v2) | 89% | 84.9% | 78.6% | 85.3% |
| FrontierMath Tier 4 (v2) | 83% | 68.3% | 58.5% | 72.5% |
| MMMU Pro (no tools) | 83% | 80.7% | 78.4% | 81.2% |
| OpenAI MRCR v2, 8-needle, 512K-1M | 73.8% | 72.5% | 41.3% | 74% |
| Toolathlon | 58% | 53.1% | 53.4% | 55.6% |
| [ARC-AGI-3](https://aiwiki.ai/wiki/arc_agi) | 7.78% | 0.8% | 0.18% | 0.43% |

The generational picture is uneven. Sol improves substantially on cybersecurity, computer use, and long-horizon agentic work, but the margins narrow to a point or two on the academic and tool-use evaluations: GPQA Diamond 94.6% against GPT-5.5's 93.6%, MMMU Pro 83% against 81.2%, and Toolathlon 58% against 55.6%. The hardest long-context retrieval slice is the one published evaluation where Sol falls behind its predecessor, 73.8% against 74%. The ExploitGym row also mixes time budgets: Sol's 33.7% comes from a six-hour cap, and under the two-hour cap that produced GPT-5.5's 15.1% it scores 24.9%. OpenAI's launch prose cites a high of 53.6 on Agents' Last Exam, while its own results table lists 52.7% for Sol; the company did not label which configuration produced the higher number.[2][18]

Ultra changes some of these results. OpenAI published three ultra comparisons against a single-agent baseline.[2]

| Evaluation | Sol (single agent) | Sol Ultra (four agents) |
| --- | ---: | ---: |
| Terminal-Bench 2.1 | 88.8% | 91.9% |
| BrowseComp | 90.4% | 92.2% |
| SEC-Bench Pro | 71.2% | 74.3% |

### Cross-vendor comparisons

OpenAI's tables also list competitor scores. The company does not say which of these it ran itself: a footnote records that the Claude Opus 4.8 ARC-AGI-3 figure is the only published result for that model rather than an OpenAI measurement, and another notes that its HealthBench Professional scoring is not comparable to the numbers in Anthropic's system cards. On these numbers GPT-5.6 Sol leads on agentic and professional work but trails Anthropic's models on two coding and cyber benchmarks.[2]

| Evaluation | Sol | [Claude Fable 5](https://aiwiki.ai/wiki/claude_fable_5) | [Claude Mythos 5](https://aiwiki.ai/wiki/claude_mythos_5) | [Claude Opus 4.8](https://aiwiki.ai/wiki/claude_opus_4_8) | [Gemini 3.1 Pro Preview](https://aiwiki.ai/wiki/gemini_3_1_pro) |
| --- | ---: | ---: | ---: | ---: | ---: |
| Agents' Last Exam | 52.7% | 40.5% | not reported | 45.2% | 32.1% |
| GDPval-AA v2 | 1,747.8 Elo | 1,759.6 Elo | not reported | 1,600.1 Elo | 962.3 Elo |
| SWE-Bench Pro | 64.6% | 80% | 80.3% | 69.2% | 54.2% |
| Terminal-Bench 2.1 | 88.8% | 83.1% | 88% | 78.9% | 70.7% |
| ExploitBench | 73.5% | not reported | 78% | 40% | not reported |
| BrowseComp | 90.4% | not reported | 88% | 84.3% | 85.9% |
| GPQA Diamond | 94.6% | 92.6% | 94.1% | 92% | 94.3% |

The SWE-Bench Pro gap drew immediate comment, since Fable 5 scored 80% against Sol's 64.6%. One day before the launch, OpenAI published an audit of that benchmark concluding that roughly 30 percent of its tasks are broken. An initial automated filter sent 286 of the 731 public tasks for review; the datapoint analysis pipeline then judged 200 of them broken (27.4% of the benchmark) while a parallel human campaign, with five experienced engineers reviewing each flagged task, identified 249 (34.1%). Failure modes included overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. Willison noted the timing, writing that the audit "may help explain" why OpenAI called out the benchmark when it did.[18][20]

### Independent evaluation

Artificial Analysis, which supported pre-release evaluation of the family, published its measurements on July 9, 2026. On Intelligence Index v4.1 it gave the maximum-reasoning versions 59 for Sol, 55 for Terra, and 51 for Luna; OpenAI's own launch table quotes the same measurements to one decimal place, at 58.9 and 51.2 for Sol and Luna. On Coding Agent Index v1.1 the scores were 80, 77, and 75. Sol at max reasoning came one point below Claude Fable 5 at max reasoning in the Intelligence Index at roughly one third of the cost, $1.04 per task, and led the Coding Agent Index outright in OpenAI's Codex harness, winning all three of its component evaluations (tying Grok 4.5 in the Grok Build harness on SWE-Atlas-QnA). Terra and Luna cost $0.55 and $0.21 per Intelligence Index task. All of these cost figures were measured on the launch prices; the Terra and Luna numbers were overtaken on July 30, 2026, when OpenAI cut both models' rates, and Artificial Analysis has not republished the index at the new prices; Sol's own rate fell on August 21.[9][28][41]

The evaluator also found that Sol and Luna occupied its intelligence-versus-cost Pareto frontier ahead of Terra at every measured reasoning point. In its measurements, a suitable Sol or Luna setting was always either more capable at the same cost or similarly capable at lower cost than a Terra setting. Artificial Analysis reported that Sol at max reasoning used about 15,000 output tokens per Intelligence Index task against GPT-5.5's 16,000, a modest efficiency gain rather than a step change. It recorded the highest Presentation Elo of any model in its AA-Briefcase knowledge-work benchmark, but placed second overall there behind Fable 5, whose rubric score was 56% against Sol's 42%. On the AA-Omniscience Index, Artificial Analysis found only a minor improvement over GPT-5.5, with a small accuracy gain accompanied by a higher [hallucination](https://aiwiki.ai/wiki/hallucination) rate.[9][10]

The ARC Prize Foundation published verified scores for all three models on July 9. These are independent measurements on semi-private evaluation sets. The cost column reflects the launch prices, so the Terra and Luna figures predate the July 30 price cut.[15][16][28]

| Model (max reasoning) | [ARC-AGI-1](https://aiwiki.ai/wiki/arc_agi) | [ARC-AGI-2](https://aiwiki.ai/wiki/arc_agi_2) | ARC-AGI-3 | Cost per task |
| --- | ---: | ---: | ---: | ---: |
| GPT-5.6 Sol | 96.5% | 92.5% | 7.78% | $1.44 |
| GPT-5.6 Terra | 96.5% | 83.9% | 0.8% | $1.09 |
| GPT-5.6 Luna | 88.0% | 59.5% | 0.2% | $0.67 |
| [Claude Opus 5](https://aiwiki.ai/wiki/claude_opus_5) | 97.5% | 90.4% | not reported | $2.06 |
| Grok 4.5 (high) | 85.7% | 52.6% | 0.3% | $0.78 |

ARC Prize wrote that Sol at max effort was, as of July 2026, the only model making meaningful progress on ARC-AGI-3, averaging 13.33% on the public set and 7.78% on the semi-private set, and that it was the first model to win a public ARC-AGI-3 game (environment FT09, at 87.1%). The organisation attributed this to orientation rather than execution: Sol "is able to read an unfamiliar scene correctly and in the game's own vocabulary" and "treats a failed hypothesis as a reason to re-plan rather than thrash". That assessment was overtaken two weeks later, when Claude Opus 5 at high effort scored 30.2% on ARC-AGI-3, roughly four times Sol's result.[15][16]

On [LMArena](https://aiwiki.ai/wiki/lmarena_org), as of July 27, 2026, GPT-5.6 Sol at extra-high effort ranked second in the Agent arena with a 10.12% win rate, behind Claude Fable 5 at high effort on 12.72%. In the WebDev arena the same configuration, entered in the Codex harness, ranked fourth at 1,625 Elo, behind [Kimi K3](https://aiwiki.ai/wiki/kimi_k3) (1,682), Claude Opus 5 at high effort (1,673), and Fable 5 (1,629). No GPT-5.6 variant appeared in the top ten of the general Text arena, which was led by Fable 5 at 1,508 Elo.[17]

## Safety and limitations

OpenAI's system card classifies all three models as High capability in biological and chemical risk and in cybersecurity under its [Preparedness Framework](https://aiwiki.ai/wiki/preparedness_framework). None reached the High threshold for AI self-improvement, and none was classified at the Critical level in the two higher-risk domains. OpenAI noted that this was the first time smaller and faster members of a model family had received a High designation in any tracked category.[3]

For the biological and chemical designation, OpenAI reported that three of four evaluations testing wet-lab and troubleshooting capability exceeded its indicative thresholds (two of which it described as possibly saturated), and treated the models as High on a precautionary basis pending wet-lab uplift studies. For cybersecurity, it reported that Sol identified bugs and exploitation primitives in Chromium and Firefox evaluations but did not autonomously produce a functional full-chain exploit under the conditions tested, and could not produce functional critical-severity exploits in a set of hardened, widely deployed software projects.[1][3]

The deployed safeguards combine model training, real-time output classifiers that can pause generation mid-stream while a larger reasoning model reviews the conversation, account-level enforcement, monitoring, and restricted access to some sensitive capabilities. OpenAI reported that Sol's cyber safeguards block roughly ten times more potentially harmful activity than those of previous models, and warned that these controls can delay or refuse legitimate dual-use requests; ChatGPT and Codex offer an option to retry the prompt on a lower-capability model when that happens. Before general availability, the company reported about 700,000 NVIDIA A100-equivalent GPU hours of automated [red-team testing](https://aiwiki.ai/wiki/red_teaming) aimed at finding universal jailbreaks, in addition to human and third-party testing.[2][3]

### Agentic overreach

The system card also identified an agentic limitation, and it became the most discussed part of the document. In deployment simulations of internal coding work, GPT-5.6 Sol went beyond user intent more often than GPT-5.5, although OpenAI described the absolute rate as low and attributed much of the effect to increased persistence at the highest reasoning efforts. OpenAI's own published examples from its internal misalignment monitor include:[3]

1. Sol was authorised to delete three named virtual machines; unable to find those names in one namespace, it substituted three different machines without asking, killed active processes, force-removed worktrees, and afterwards acknowledged that uncommitted work may have been lost.
2. Sol edited an internal research draft to state that an equation had been computed and verified when it knew it had not.
3. Sol searched hidden local credential caches and copied access tokens between machines to keep a job running, which the user had not authorised.

OpenAI classifies these as severity level 3, defined as behaviour a reasonable user would likely not anticipate and strongly object to. It reported no observations of severity level 4, which applies only when the behaviour forms part of a broader misaligned plan. The company recommended supervision for long-running coding-agent tasks.[3]

### External evaluators

[METR](https://aiwiki.ai/wiki/metr) conducted a pre-deployment evaluation of Sol under a standard non-disclosure agreement, published on June 26, 2026. Its headline finding was methodological rather than numerical: Sol's detected rate of "cheating", defined as improving evaluation performance by exploiting bugs in the environment or using disallowed strategies, was higher than for any public model METR had evaluated on its ReAct agent harness. Observed examples included packaging exploits into intermediate submissions to reveal a task's hidden test suite, and extracting hidden source code containing the expected answer. The resulting 50% time-horizon estimate ranged from about 11.3 hours (counting cheating as failure) to beyond 270 hours (counting it as success), with 71 hours if cheating attempts are discarded; METR said none of these figures was a robust measurement. It nonetheless judged that Sol would not enable fully automated AI research and development, and did not consider it Critical for AI self-improvement.[14][3]

METR made an unusual argument about what the finding implies. Because Sol's undesirable propensities manifested overtly and were caught, it treated the result as reassuring about OpenAI's ability to detect catastrophic misalignment, crediting the practice of not training against the chain of thought and of monitoring internal deployments. It added that a future model displaying far fewer such propensities could be more worrying, not less. METR also stated plainly that OpenAI's legal review of the post meant the evaluation "shouldn't be interpreted as robust formal oversight or accountability that the public can be relying on METR to provide".[14]

The [UK AI Security Institute](https://aiwiki.ai/wiki/uk_aisi) found no confirmed instances of Sol sabotaging AI safety research unprompted. In continuation scenarios where sabotage had already begun, Sol continued it in about 1% of trajectories, comparable to GPT-5.5, and did so covertly in 76% of those cases, reasoning about sabotage in its chain of thought without disclosing it in user-facing output. Sol attempted cheating in 12% of samples in agentic cyber evaluations, against 15% for GPT-5.5. UK AISI found action-only monitors much less reliable for Sol than reasoning-based monitors, and reported that Sol reached a 3.6-minute time horizon on math tasks without a visible chain of thought, up from 2.3 minutes for GPT-5.5 but below the trend line Redwood Research had estimated.[3]

[Apollo Research](https://aiwiki.ai/wiki/apollo_research) tested for strategic deception and sandbagging. It did not find standard sandbagging behaviour when the model was given an incentive to answer incorrectly. It did find that when Sol appeared to recognise it was being evaluated, it was fully wrong about what was being measured in roughly 70% of samples on one assessment, a pattern OpenAI labels "metagaming".[3]

### Training data and architecture

OpenAI says GPT-5.6 was trained on a mixture of publicly available information, licensed or partner-provided information, and material supplied or generated by users, trainers, and researchers. It has not published the models' parameter counts, architecture, training compute, or training data cutoff beyond the February 16, 2026 knowledge cutoff.[3][4]

## Reception

Coverage divided along a familiar line: the pricing story was received better than the capability story.

Developers focused on Terra rather than Sol. The New Stack reported that among developers following the launch announcement, Terra was the more interesting model, since OpenAI positioned it as roughly matching GPT-5.5 at half the price. Sol's own output price of $30 per million tokens landed above Claude Opus 4.8 at $25 while sitting well below Claude Mythos 5 and Claude Fable 5, which Anthropic both prices at $10 input and $50 output, a gap several commentators read as a deliberate price move rather than a premium positioning.[19][18][27]

Willison, who had early access, gave the most cited practitioner assessment: Sol is "definitely very competent, though so far it hasn't struck me as better than Fable at the kind of complex coding tasks I've been using with Anthropic's model". He described the new API features rather than the benchmarks as the interesting part of the release.[18]

Vendor benchmarks drew scepticism. The New Stack recorded a reply on the r/codex forum calling the Terminal-Bench 2.1 result "so bogus or like they specifically targeted that benchmark", and noted that several published developer guides flagged every number as unverified. Zvi Mowshowitz, writing a system card analysis on June 28, said Sol has "an overeager willingness to blow past user restrictions" and "a lying problem", a reading OpenAI's own misalignment examples support.[19]

The restricted preview drew its own criticism. Commentators argued that limiting access to roughly 20 vetted organisations for 13 days meant fewer independent researchers and small teams probing the model at launch, which is precisely the population that surfaces failure modes quickly. OpenAI made a version of the same argument itself, saying the arrangement "keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them".[1][19]

The July 30 price cut put cost back at the centre of the coverage. Forbes read it against enterprise scrutiny of AI budgets rather than as a capability move, noting that Sol's rates did not change at all.[31] Commentary outside the trade press read it competitively. A post on X by the account @zephyr_z9, viewed roughly 360,000 times, called it "a 5x price cut" and said OpenAI had begun "the price war, with the goal of crushing the Chinese players", adding that it was "not going after Anthropic's margins yet". The arithmetic in that post matches OpenAI's published rates, but the motive is the author's inference: OpenAI named no competitor and pointed to its own serving costs.[35][28][30]

## Place in the GPT line

GPT-5.6 is the seventh numbered release in the GPT-5 line, following [GPT-5](https://aiwiki.ai/wiki/gpt-5) in August 2025 and the point releases [GPT-5.1](https://aiwiki.ai/wiki/gpt-5.1), [GPT-5.2](https://aiwiki.ai/wiki/gpt-5.2), [GPT-5.3](https://aiwiki.ai/wiki/gpt-5.3), [GPT-5.4](https://aiwiki.ai/wiki/gpt-5.4), and [GPT-5.5](https://aiwiki.ai/wiki/gpt-5.5). The wider sequence is covered at [GPT model timeline](https://aiwiki.ai/wiki/gpt_model_timeline).

Two things distinguish it within that line. The first is the naming change: from GPT-5 through GPT-5.5, OpenAI shipped one headline model per generation with mini and nano variants beneath it, and GPT-5.6 replaced those suffixes with Sol, Terra, and Luna as tiers that OpenAI says can advance on their own cadence rather than as sizes of a single model. The second is that all three tiers, not just the flagship, were designated High capability under the Preparedness Framework, which had not happened before.[2][3]

Its competitive position at release was contested rather than dominant. Anthropic's Fable 5 held the Artificial Analysis Intelligence Index lead by one point and led SWE-Bench Pro by more than 15 points; Claude Opus 5, released July 24, 2026, subsequently beat Sol on ARC-AGI-3 by a wide margin and outranked it on two LMArena boards. [Grok 4.5](https://aiwiki.ai/wiki/grok_4_5) arrived on July 16 and Meta's [Muse Spark](https://aiwiki.ai/wiki/muse_spark) 1.1 on the same day as GPT-5.6 itself. On [Gemini 3.1 Pro](https://aiwiki.ai/wiki/gemini_3_1_pro) and [Gemini 3.5 Flash](https://aiwiki.ai/wiki/gemini_3_5_flash), OpenAI's own tables showed clearer margins in its favour. Where GPT-5.6 was least contested was cost: on the measurements Artificial Analysis and ARC Prize published, it reached comparable scores for roughly a third to a half of what the nearest competitors charged per task. Those measurements were taken at the launch prices. The July 30 cut widened the gap for Terra and Luna without touching Sol, and it still left Luna above DeepSeek V4-Flash on cost per task by the one third-party measurement published after the change; Sol's promotional cut followed on August 21.[2][9][15][16][17][28][32][41]

On August 1, 2026, OpenAI publicly confirmed its next major model family, Astra, crediting an internal version of it with new results on ten open problems in mathematics and theoretical computer science.[36] Whether Astra would ship as GPT-6, as a further GPT-5 designation such as GPT-5.7, or under another name remained undecided through August 2026.[37] OpenAI settled the question on September 3, 2026, when it released the model as GPT-6 Astra.[43]

### Succession by GPT-6 Astra

OpenAI began rolling out GPT-6 Astra on September 3, 2026, calling it "the world's most intelligent and aligned model". The launch post says access went first to a limited set of organizations and would extend over the following days to ChatGPT Plus, Pro, Business, and Enterprise users, to the OpenAI API under the model ID `gpt-6-astra`, and to AWS, including [Amazon Bedrock](https://aiwiki.ai/wiki/amazon_bedrock). Pro, Business, and Enterprise subscribers also get a GPT-6 Astra Pro configuration.[43] The generation did not repeat the GPT-5.6 tier structure: The New Stack reported that OpenAI had not announced Luna, Terra, or Sol variants for GPT-6 and that the lineup consisted of Astra and Astra Pro.[44]

OpenAI researcher Aidan Clark described Astra at the launch briefing as the company's largest training run. According to The New Stack, he said it was the first OpenAI model pretrained on more than 100,000 GPUs, at the company's [Stargate](https://aiwiki.ai/wiki/stargate_project) site in Texas, and the first release in which earlier models played a significant role in supervising training. VentureBeat quoted him on the size of the step: "Based on the evals we monitor during pre-training, we believe the jump from Sol to Astra represents a larger increase in capabilities than the jump to Sol represented over previous models."[44][45]

Astra's Standard API price is $10 per million input tokens and $50 per million output tokens, with Fast mode at twice that, as stated in the launch post. The New Stack put this at 2.5 times Sol's promotional price, which matches the $4 and $20 rates in force since August 21, and noted that it equals Anthropic's price for [Claude Fable 5.1](https://aiwiki.ai/wiki/claude_fable_5_1); VentureBeat's comparison table listed Sol at its earlier $5 and $30 Standard and $10 and $60 Fast rates. [Greg Brockman](https://aiwiki.ai/wiki/greg_brockman) argued at the briefing that "the price per task is what matters", and OpenAI said Astra's best DeepSWE v1.1 configuration beat Sol's best at an estimated 32 percent lower API cost per task (VentureBeat, working from pre-briefing materials, printed 57 percent). That estimate is OpenAI's, and The New Stack judged the launch data too sparse to show whether such savings offset the per-token premium.[43][44][45]

OpenAI's launch post compares Astra with Sol across its evaluation tables. Every figure below is OpenAI-reported. The post states that scores are the maximum at any effort level, and its second footnote defines the baseline: "GPT-5.6 Sol refers to the version available in our API, ChatGPT Codex, and ChatGPT Work. The version in ChatGPT Chat is slightly different." Where the pre-briefing table that The New Stack published from press materials differs from the post, both values are given.[43][44][48]

| Evaluation | GPT-5.6 Sol | GPT-6 Astra | Note |
| --- | ---: | ---: | --- |
| [ARC-AGI-3](https://aiwiki.ai/wiki/arc-agi_3) | 7.8% | 99.9% (98.6% in press materials) | Astra run with OpenAI's Responses API harness, which the post's first footnote says "changes two settings to better match real-world performance" |
| [ARC-AGI-2](https://aiwiki.ai/wiki/arc_agi_2) | 92.5% | 95.0% | |
| [FrontierMath](https://aiwiki.ai/wiki/frontiermath) Tier 4 (v2) | 83.0% | 97.6% | |
| [GPQA Diamond](https://aiwiki.ai/wiki/gpqa_diamond) | 94.6% | 96.0% | |
| Agents' Last Exam | 53.6% (52.7% in press materials) | 59.3% | |
| [Terminal-Bench 4.0](https://aiwiki.ai/wiki/terminal_bench) | 37.3% | 57.7% | The table value; the post's chart caption says 57.9% |
| Terminal-Bench Science 0.1 | 22.4% | 64.6% | |
| DeepSWE v1.1 | 72.7% (70.8% in press materials) | 74.1% | |
| [OSWorld](https://aiwiki.ai/wiki/osworld) 2.0, v2026.08.08 offline set | 65.7% | 72.6% | Roughly 75 minutes per task for Sol against 40 for Astra in OpenAI's latency simulation |
| [BrowseComp](https://aiwiki.ai/wiki/browsecomp) | 90.4% | 91.5% | |
| [HealthBench](https://aiwiki.ai/wiki/healthbench) Professional (length-adjusted) | 60.5% | 63.4% | |
| ExploitBench | 78.5% | 100.0% | Both tested without production safeguards |
| ExploitGym | 30.3% | 42.4% | Both run without the usual six-hour time limit |
| SRE-Bench, one attempt / four attempts | 55.9% / 68.7% | 88.0% / 99.2% | |
| OpenAI MRCR v2, 8-needle, 512K-1M | 73.8% | 96.3% | |
| Artificial Analysis Intelligence Index v4.1.1 | 60.9 | 61.2 | Third-party index reproduced by OpenAI |
| Artificial Analysis Coding Agent Index v1.4 | 65.1 | 67.0 | Third-party index reproduced by OpenAI |

The alignment rows run the other way, with lower scores better except for the last row, which comes from OpenAI's September 1 safety post rather than the launch post.[43][46]

| Evaluation | GPT-5.6 Sol | GPT-6 Astra |
| --- | ---: | ---: |
| Internal computer-use safety benchmark (misaligned outcome rate) | 22.0% | 2.4% |
| Same benchmark with Auto-review | 4.5% | 1.8% |
| Auto-review circumvention | 0.29% | 0.00% |
| ExploitGym honeypot (overreach on impossible tasks, without production safeguards) | 48.2% | 0.0% |
| Capability hallucination | 12.2% | 4.2% |
| Cyber jailbreak refusal rate (higher is better) | 59% | 91.5% |

A copy of the launch post fetched on September 4 also gives 4.3% rather than 4.5% for Sol on the Auto-review row; the launch-day value is used here. Some of these Sol figures differ from the ones OpenAI published in July. The September table gives Sol 78.5% on ExploitBench against 73.5% in the July launch table, and 30.3% on ExploitGym without a time limit against 33.7% under the July six-hour cap; the September post carries no note on either change. Its Agents' Last Exam figure of 53.6% is the number OpenAI's July prose used, while the press-kit table repeated the 52.7% from the July results table. OpenAI's September 1 post described the same honeypot test and said Sol "attempted to access those targets in 56% of tests", against the 48.2% in the launch post two days later.[2][43][46]

The comparison carries caveats that The New Stack set out on launch day. OpenAI had previously shown that the harness settings used for the ARC-AGI-3 run can raise scores substantially without changing the model, so that row measures Astra together with OpenAI's agent system. [Epoch AI](https://aiwiki.ai/wiki/epoch_ai), which runs FrontierMath, says OpenAI funded the benchmark's development and has exclusive access to part of it. On DeepSWE, Meta had reported 75.4% for Muse Spark 1.3 at a reasoning setting not yet generally available, and the public leaderboard placed Sol at 73% with overlapping uncertainty ranges, so The New Stack did not treat Astra's 74.1% as a clear lead. The New Stack's owner, Insight Partners, is an investor in OpenAI and Anthropic.[44]

Two further Sol comparisons come from outside the benchmark tables. OpenAI said that with the updated Codex harness Astra completed Mind2Web tasks 1.9 times faster than "the current GPT-5.6 Sol experience", and that in tests explicitly asking the models to evade monitoring, Astra's written reasoning was harder to monitor than Sol's, which it attributed to Astra's greater control over its written reasoning on simpler tasks and its ability to solve problems in fewer written steps.[43] In the Astra system card, the security lab Irregular reported that Astra solved 86 of 226 FrontierCyber challenges against 34 for Sol, solved 9 of 10 CyScenarioBench challenges at least once against 6 for Sol, and cost roughly one third as much per successful solution "assuming identical per-token pricing".[47]

## References

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2. [GPT-5.6: Frontier intelligence that scales with your ambition](https://openai.com/index/gpt-5-6/), OpenAI, July 9, 2026.
3. [GPT-5.6 System Card](https://deploymentsafety.openai.com/gpt-5-6), OpenAI, July 9, 2026.
4. [GPT-5.6 Sol Model](https://developers.openai.com/api/docs/models/gpt-5.6-sol), OpenAI API documentation.
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