openai
OpenAI: GPT-5.1-Codex
openai/gpt-5.1-codex
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-5.1-codex as an OpenAI GPT-5.1 variant tuned for software engineering and coding workflows, for both interactive sessions and longer autonomous tasks. The same listing reports text plus image inputs, text outputs, a large context window, mandatory reasoning with low, medium and high effort settings, and support for tools and structured outputs. These are catalog claims; an independent model card was not available in this source pack.[1]
Strengths
- Source-reported specialization for software engineering and coding workflows, including interactive development and extended independent execution.[1]
- Catalog-listed multimodal input path with text and image in, text out, plus support for tools, tool choice, structured outputs, response format, seed and verbosity controls.[1]
- Reasoning is mandatory rather than optional, with reported low, medium and high effort settings and medium as the default effort.[1]
Limitations
- Evidence is catalog-only: this pack lacks an independent model card, so capabilities and behavior should be validated on the intended workload before adoption.[1]
- The listing includes Design Arena measurements across agent and model categories, but Artificial Analysis data is null; scores and rankings are not restated here.[1]
- License terms, hardware needs, knowledge cutoff and safety behavior are not specified; output is text-only and reasoning cannot be disabled according to the catalog.[1]
Where to use it
- Potentially suitable for coding assistants, refactoring, test generation and engineering agents where tool use and structured responses matter, subject to evaluation.[1]
- May fit workflows that combine screenshots or diagrams with text instructions, because the catalog reports image input with text output.[1]
- Could be considered for longer coding tasks that benefit from adjustable reasoning effort, while checking cost, latency and reliability in practice.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5.1-CodexSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....
Specifications
- Model ID
- openai/gpt-5.1-codex
- Canonical ID
- openai/gpt-5.1-codex-20251113
- Creator
- openai
- Added to OpenRouter
- 13.11.2025
- Context length
- 400,000
- Modality
- text+image->text
- Input modalities
- text, image
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $1.25 / 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 | No measurement | reasoning: mandatory · default medium · efforts: 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: high, medium, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| 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 |
|---|---|---|---|---|
| Design Arena | agents · fullstackwin rate 44.5% · rank 31 | 1,086Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 53.4% · rank 22 | 1,191Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 44% · rank 47 | 1,000Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 54.6% · rank 91 | 1,143Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 55.9% · rank 55 | 1,211Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 50.5% · rank 86 | 1,128Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 60% · rank 52 | 1,228Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 56.1% · rank 93 | 1,155Elo | CC — below the B band | 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) |
|---|---|---|---|---|---|---|
| Azure | 400,000 | $1.25 | $10 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.