GPT-5.6 Sol
Released
GPT-5.6 Sol is an AI model released by OpenAI on Friday, Jun 26 2026, 4 days after GPT-5.5-Cyber. Benchmark results (shown below) cover BrowseComp, CheatBench, BullshitBench v2, Gray Swan IPI, ProgramBench, CursorBench 4.0, and 10 more.
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API pricing
Up to 272K input tokens
- InputWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it.
- $5.00
- Cached inputA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again.
- $0.50
- Cache writeA one-off charge for storing text so later requests can reuse it at the cheaper cached rate.
- $6.25
- OutputWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one.
- $30.00
Over 272K input tokens
- InputWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it.
- $10.00
- Cached inputA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again.
- $1.00
- Cache writeA one-off charge for storing text so later requests can reuse it at the cheaper cached rate.
- $12.50
- OutputWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one.
- $45.00
- Reasoning or thinking is supported.
USD per 1M tokensEvery price here is for one million tokens. A token is roughly three-quarters of a word, so a million tokens is about 750,000 words of text.Model mappingVerified August 18, 2026Read from developers.openai.com. Click the check to open the source.
Available from
| OpenAI | — | flex | $1.00 | $5.00 | 1M |
|---|---|---|---|---|---|
| OpenAI | — | — | $2.00 | $10.00 | 1M |
| OpenAI | — | fast | $4.00 | $20.00 | 1M |
| Azure | — | — | $4.00 | $20.00 | 1M |
| Azure | eu | — | $4.40 | $22.00 | 1M |
| Azure | us | — | $4.40 | $22.00 | 1M |
| Amazon Bedrock | us-east-1 | — | $4.40 | $22.00 | 1M |
Benchmarks
Coding
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better.via ProgramBench
1%
#3 of 20Best: Claude Opus 5 · 4.5%
CursorBench 4.0Agentic coding — Cursor's own test of coding agents on ambiguous, multi-file tasks taken from real Cursor sessions — editing, refactoring, investigating a codebase, understanding what the user meant, managing jobs and following a design. Cursor runs each model at several reasoning efforts; each release here carries the score of its best listed effort. Scores aren't comparable with earlier CursorBench versions. Higher is better.via CursorBench
41.7%
#6 of 13Best: Claude Opus 5.5 · 57.8%
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better.
73%
#6 of 32Best: Gemini 4 Argon · 77.9%
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better.
77%
#12 of 32Best: Claude Fable 5.1 · 97%
Supabase EvalsSupabase coding — Supabase's own open benchmark: a coding agent is dropped into a real Supabase project and asked to do real work — set up a schema, fix a broken security policy, debug an Edge Function — and every run is checked against a live Supabase stack. This is the headline number, where the agent has Supabase's own skills loaded, as most people building on Supabase would. The score is the share of scenarios it got right. Higher is better.
84.1%with skills
58%no skills
with skills
#5 of 11Best: Claude Opus 5 · 92.8%
no skills
#9 of 11Best: Claude Opus 5.5 · 91.3%
Terminal & CLI
Frontier-Bench v0.1Agentic computer work — A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better.
34.4%
#2 of 9Best: Claude Opus 5 · 43.3%
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better.via Terminal-Bench
37.27%
#11 of 21Best: Claude Sonnet 5.5 · 70.6%
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better.
88.8%
#3 of 32Best: DeepSeek-V4.1-Flash · 90.6%
Agentic & tool use
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better.via BenchLM
92.2%
#1Best published BrowseComp score of all tracked models
BU BenchBrowser agent — Can the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better.
67%
#3 of 7Best: Claude Opus 4.8 · 74%
Reasoning & science
Knowledge work
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo)
1748
#8 of 20Best: Claude Opus 5 · 1861
Robustness
CheatBenchCheating rate — Measures how often AI agents try to cheat on difficult assignments, such as reading hidden answers, copying work or manipulating grading. The overall score gives equal weight to ten categories; the sycophancy category measures how far an agent shifts its beliefs toward a user's stated views. Scores describe each model in its tested agent setup, and task success is measured separately. Lower is better.via CheatBench
77%
#10 of 12Best: Claude Opus 5.5 · 11.2%
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better.
48%
#36 of 84Best: Claude Opus 4.8 · 95%
Gray Swan IPIPrompt injection robustness — Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. Gray Swan's indirect prompt injection benchmark measures how often such an attack succeeds when the attacker gets a single try. Lower is better.
3.1%k = 1
16.3%k = 10
20%k = 15
k = 1
#7 of 13Best: Claude Opus 5 · 0.2%
k = 10
#6 of 13Best: Claude Opus 5 · 1.6%
k = 15
#7 of 14Best: Gemini 4 Argon · 0.7%
Community preference
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better.
1367
#14 of 18Best: Claude Opus 5.5 · 2074
Source: CursorBench, retrieved 28 September 2026 · Source: CheatBench, retrieved 25 September 2026 · Source: ProgramBench, retrieved 11 September 2026 · Source: Terminal-Bench, retrieved 30 August 2026 · Source: BenchLM, retrieved 24 August 2026. Other sources are identified on the linked benchmark pages.
Compare GPT-5.6 Sol with
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GPT-5.6 Sol was released by OpenAI on Friday, Jun 26 2026.
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