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OpenAI: GPT-5.6 Sol
OpenAI
•
GPT-5.6 Sol (max)
•
Released 
July 2026

OpenAI
GPT-5.6 Sol (max)

OpenAI's flagship GPT-5.6 model for long-horizon agentic coding, terminal-heavy workflows, and token-efficient frontier reasoning.

Modality:
Text
Image
PDF
model ID
openai/gpt-5.6-sol
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Output Speed *

62.03
tok/s

Intelligence Index *

47.1
/ 100

Context Window *

1050000
tokens

Input price

12
Anytoken

Output price

60
Anytoken
GPT-5.6 Sol: Frontier Agentic Coding at Lower Token Cost GPT-5.6 Sol is the flagship tier of OpenAI's GPT-5.6 family, sitting above Terra (balanced) and Luna (fast, low-cost). It targets frontier reasoning and long-horizon agentic work, with particular strength in command-line and multi-step coding tasks. Its defining trait is token efficiency: OpenAI reports Sol reaches state-of-the-art coding-agent results while using far fewer output tokens than comparable frontier models. Teams running long-running coding agents, terminal automation, and multi-step engineering workflows benefit most. The `gpt-5.6` alias routes to Sol; snapshots let you lock behavior for production. Start building with GPT-5.6 Sol via the AnyAPI.ai API

Performance

Where GPT-5.6 Sol Leads: Coding Agents at Fewer Tokens

GPT-5.6 Sol is built for long-horizon agentic coding and terminal-driven engineering. On the Artificial Analysis Coding Agent Index, Sol at max reasoning set a state-of-the-art score of 80, while using less than half the output tokens and completing tasks in less than half the time of the nearest frontier competitor. The practical value is efficiency, not just headline accuracy: fewer tokens per solved task lowers the effective cost of running persistent coding agents. For teams operating multi-hour agent runs across real repositories, that efficiency directly reduces spend and wall-clock time per completed task in production.

Benchmarks

GPT-5.6 Sol Benchmarks: Coding, Intelligence, and Terminal Tasks

On the Artificial Analysis Intelligence Index v4.1, GPT-5.6 Sol at max reasoning scored 59, one point below the leading Claude Fable 5, at roughly one-third of the cost per task. On the Coding Agent Index it led at 80 in OpenAI's Codex harness. OpenAI reports Terminal-Bench 2.1 results of 88.8% for Sol and 91.9% in Ultra mode. Independent testing notes Sol trails some competitors on SWE-bench Pro-style repository bug-fixing, so its edge is clearest in terminal use, long-horizon agentic workflows, and token efficiency rather than every coding benchmark.

Output Speed

*
62.03
tok/s

Intelligence Index

*
47.1
/ 100

MMLU *

Broad world knowledge and problem-solving
0
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
94
%

HLE *

Adherence to multi-step structured instructions.
50
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
0
%

Technical Specifications

What the model supports

GPT-5.6 Sol accepts text and image input and returns text. It offers a 1.05M-token context window with a separate 128K-token maximum output per response—the two limits are independent, so long inputs do not extend how much the model can generate. Reasoning effort is configurable across multiple levels up to max, letting teams trade latency and token cost against depth. Higher reasoning effort improves stability, notably on large images, but increases token use. Function calling and JSON-schema structured outputs are supported, making Sol suitable for tool-driven agentic pipelines.
Verified Specifications — 
GPT-5.6 Sol (max)
*
Input modalities
Text
Image
PDF
output modalities
Text
Context window
1050000
 tokens
Maximum output tokens
128000
Reasoning
Yes
Knowledge cutoff
July 2026
Pricing (standard)
12
 AnyTokens in
 / 
60
 AnyTokens out

Limitations & Trade-offs

Where GPT-5.6 Sol (max) falls short

1
Slower output than average. Independent measurement from Artificial Analysis places GPT-5.6 Sol below the median output speed for its class, and time-to-first-token is measured at several seconds at p95. For interactive chat or real-time UIs where perceived responsiveness matters, this latency is a genuine drawback. Sol suits background agent runs and batch engineering tasks better than latency-sensitive front-end experiences; a faster tier such as Luna is preferable for real-time products.
2
Repository bug-fixing is not its strongest axis. Independent comparisons show GPT-5.6 Sol trailing some frontier competitors on SWE-bench Pro-style benchmarks that measure fixing bugs in real repositories. Sol's clearest advantages are terminal use, long-horizon agentic workflows, and token efficiency. If your core task is opening a GitHub issue and getting a correct first-pass fix, a model that leads SWE-bench Pro may deliver better outcomes.
3
Reasoning overhead and cost at high effort. Sol sits in a premium reasoning tier, and higher reasoning-effort settings materially increase token usage and latency. While Sol is token-efficient relative to peers at equivalent tasks, max reasoning still produces large output volumes and higher spend. For simple or high-volume everyday generation, Terra or Luna occupy lower-cost tiers and are more economical.
4
Vision instability on large images. OpenAI confirmed that Sol becomes less stable on images around 2,000×2,000 pixels or larger, especially at lower reasoning effort, sometimes returning misplaced detection boxes. Higher reasoning effort improves stability but raises token cost. Teams doing high-resolution detection or grounded visual extraction should validate at their image sizes and may need elevated reasoning effort, which affects throughput and spend.

Best-Fit Workloads

Where this model earns its place

01
Long-horizon coding agents
‍

Sol is designed for multi-step, multi-hour agent runs across real codebases. On the Artificial Analysis Coding Agent Index it led at 80 while using fewer output tokens than competitors, which lowers the cost of sustained agent loops. Production CI assistants, autonomous refactoring, and PR-review agents benefit. Note that latency is higher than average, so background execution suits it better than interactive editing.

02

Terminal and CLI automation

‍
OpenAI reports strong Terminal-Bench 2.1 results (88.8% for Sol, 91.9% in Ultra), and independent reviews single out CLI-heavy agentic coding as a category where Sol excels. Shell-driven build, test, and deployment automation and command-line agent harnesses are natural fits. The hosted shell and tool-calling support make it practical to wire Sol into terminal-based agent frameworks.

03

Agentic browsing and research workflows

‍
OpenAI reports state-of-the-art BrowseComp results for Sol, with further gains in Ultra mode, reflecting strong multi-step agentic browsing. Long-running research assistants that navigate, gather, and synthesize across sources fit well, especially paired with the web search and tool-search capabilities. Reserve higher reasoning effort for the hardest tasks to balance quality against token cost.

04

Grounded multimodal document extraction

‍
Independent VLM testing rates Sol as OpenAI's strongest vision model to date for detection, counting, OCR, and extraction, and it ranks highly on multimodal and grounded workflows. Extracting structured data from screenshots, charts, and documents is a strong fit. Validate at your image resolution: stability degrades on very large images at low reasoning effort, so raise effort for high-resolution inputs.

Pricing in anytokens via AnyAPI
Input
12
₳
Output
60
₳
Cache write
15
₳
Cache read
1.2
₳
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PerformanceBenchmarksTechnical SpecificationsLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access GPT-5.6 Sol (max) via AnyAPI.ai

Access GPT-5.6 Sol (max) through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate GPT-5.6 Sol (max) without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access GPT-5.6 Sol (max) and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test GPT-5.6 Sol (max) against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use GPT-5.6 Sol (max) from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use GPT-5.6 Sol (max) for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GPT-5.6 Sol is OpenAI's flagship GPT-5.6 model, built for frontier reasoning and long-horizon agentic coding. It is strongest at command-line and multi-step coding tasks, terminal automation, and agentic browsing, and it leads the Artificial Analysis Coding Agent Index while using fewer output tokens than comparable frontier models.

GPT-5.6 Sol has a 1.05M-token context window, shared across the GPT-5.6 family. Its maximum output is a separate 128,000 tokens per response. The two limits are independent: a very long input does not increase how much the model can generate in a single completion.

Yes. GPT-5.6 Sol exposes configurable reasoning effort across multiple levels up to max. Higher effort improves quality and stability—including on large images—but increases token use and latency. Teams should match reasoning effort to task depth: lower for simple edits, higher for complex debugging or high-resolution vision work.

GPT-5.6 Sol leads on terminal-heavy agentic coding (Terminal-Bench 2.1) and token efficiency, while independent comparisons show Claude Opus 5 leading on SWE-bench Pro repository bug-fixing. Choose Sol for CLI-driven, long-horizon agent workflows; choose Opus 5 when correct first-pass fixes in real repositories are the priority.

GPT-5.6 Sol accepts text and image input, plus files such as PDFs, and returns text only. It is OpenAI's strongest vision model to date for detection, OCR, and extraction, though stability can degrade on images around 2,000×2,000 pixels or larger at low reasoning effort.

* Benchmark data source: Artificial Analysis artificialanalysis.ai