openai
OpenAI: GPT-6.1 Sol
openai/gpt-6.1-sol
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter’s catalog describes openai/gpt-6.1-sol as an OpenAI model accepting text, image, and file inputs and producing text, with reasoning always enabled and configurable effort. It is positioned as a GPT-6 Sol upgrade below GPT-6 Astra and suited to agentic coding, computer use, and document-heavy professional work. The listing reports a 1,050,000-token context window plus tools, structured outputs, seed, and verbosity controls. Evidence is catalog-only; an independent model card was not available in this source pack.[1]
Strengths
- Accepts text, image, and file inputs while returning text, according to the catalog.[1]
- Offers five reasoning-effort settings with a medium default, but reasoning remains mandatory.[1]
- Pairs a very large listed context window with tool calls, structured outputs, seed, response format, and verbosity controls.[1]
Limitations
- Only OpenRouter catalog evidence was supplied; because no independent model card was available, validate behavior on the intended workload.[1]
- Reasoning cannot be disabled, which reduces configuration flexibility for tasks that do not need it.[1]
- Listed arena outcomes were mixed: stronger in game development, 3D, and UI-component categories, less favorable for PDF and data visualization.[1]
Where to use it
- Evaluate for agentic coding, computer-use, or document-heavy professional workflows, consistent with the catalog's stated positioning.[1]
- Prototype assistants that inspect supplied images or files and produce text-only responses.[1]
- Test tool-using or structured-output applications where adjustable reasoning effort is desirable.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-6.1 SolSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional...
Specifications
- Model ID
- openai/gpt-6.1-sol
- Canonical ID
- openai/gpt-6.1-sol-20260929
- Creator
- openai
- Added to OpenRouter
- 29.09.2026
- Context length
- 1,050,000
- Modality
- text+image+file->text
- Input modalities
- file, image, text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $2 / out $10
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | SS — top 10% of the cohort | reasoning: mandatory · default medium · efforts: max, xhigh, high, medium, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default medium · efforts: max, xhigh, high, medium, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 54.6 | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 50.3% · rank 14 | 1,197Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 60.2% · rank 2 | 1,274Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 55% · rank 9 | 1,191Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 52.8% · rank 10 | 1,229Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 45% · rank 25 | 1,139Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 52.3% · rank 5 | 1,291Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 56.7% · rank 9 | 1,271Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 59% · rank 5 | 1,428Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 56.7% · rank 4 | 1,361Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 51.4% · rank 26 | 1,273Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 60% · rank 3 | 1,408Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 60.9% · rank 6 | 1,358Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 53.8% · rank 8 | 1,315Elo | SS — top 10% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| OpenAI | 1,050,000 | $1 | $5 | — | — | 100% |
| Amazon Bedrock | 1,050,000 | $2 | $10 | — | — | 100% |
| Azure | 1,050,000 | $2 | $10 | — | — | 100% |
| OpenAI | 1,050,000 | $2 | $10 | — | — | 100% |
| Amazon Bedrock | 1,050,000 | $2.2 | $11 | — | — | — |
| Azure | 1,050,000 | $2.2 | $11 | — | — | 100% |
| Azure | 1,050,000 | $2.2 | $11 | — | — | 100% |
| OpenAI | 1,050,000 | $4 | $20 | — | — | 100% |
| OpenAI | 1,050,000 | $12 | $60 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
- Amplitude's Wave agent uses OpenAI models to ship product improvements in one to two daysAmplitude says its proactive product agent Wave, powered mostly by OpenAI's GPT-6.1 Sol, GPT-6 Luna and GPT-Image-2.5 Flare models, helps teams ship product improvements in one to two days instead of roughly a month.
- Google DeepMind and Artificial Analysis Introduce MKA, a Benchmark for Visually Grounded Agent WorkMKA is a 200-task benchmark that measures whether AI agents can extract decisive evidence from blueprints, scans, and charts and turn it into professional deliverables.
- Gemini 4 Carbon opens to general availability at launch pricingSimon Willison reports that Google's Gemini 4 Carbon, previously limited to a subset of cyber defenders, is now available to everyone, with launch prices set to double on December 1st.