openai
OpenAI: GPT-6.1 Sol (batch)
openai/gpt-6.1-sol:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter's catalog lists openai/gpt-6.1-sol:batch as an OpenAI text-output variant accepting text, image, and file inputs, with a 1,050,000-token context window. The catalog associates the model with agentic coding, computer use, and document-heavy professional work, and reports support for tools, structured outputs, and adjustable reasoning effort. It marks reasoning as mandatory and reports Design Arena and Artificial Analysis benchmark entries. Because this source pack contains only catalog data and no independent model card, capabilities and workload fit require direct validation.[1]
Strengths
Limitations
- Evidence is limited to OpenRouter's catalog; an independent model card was not available in this source pack.[1]
- The listing does not explain how the :batch variant changes execution or service behavior.[1]
- Artificial Analysis coding and agentic index fields are null; reported benchmarks still need workload-specific validation.[1]
Where to use it
- Consider document-heavy professional workflows that combine long prompts with image or file inputs.[1]
- Consider agentic coding or computer-use pilots; the catalog names these areas, but production fit remains unproven.[1]
- Consider applications needing tool calling, structured outputs, or selectable reasoning effort.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-6.1 Sol (batch)Sources 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:batch
- 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 $1 / out $5
- 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 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.