openai
OpenAI: GPT-5.5
openai/gpt-5.5
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenAI’s GPT-5.5 is presented in the OpenRouter catalog as a frontier model for complex professional work, with text, image, and file inputs and text output. The catalog reports a context window above one million tokens, configurable reasoning enabled by default at medium effort, and support for tools, structured outputs, verbosity, and seeds. Its listed benchmark results cover coding, agentic, design, and content-generation tasks, but these are catalog measurements rather than guarantees. An independent model card was not available in this source pack.[1]
Strengths
- Accepts text, image, and file inputs while producing text, according to OpenRouter’s catalog.[1]
- Offers a very large context window and configurable reasoning effort, including a medium default and an option to run without explicit reasoning.[1]
- Exposes API controls for tools, structured outputs, response formatting, verbosity, token limits, and seed values.[1]
Limitations
- The evidence pack contains only OpenRouter catalog data; an independent model card was not available, so provider-described capabilities and catalog measurements require workload-specific validation.[1]
- Output is limited to text in the catalog, even though inputs can include files and images.[1]
- Reported benchmark entries span selected coding, design, and agentic tasks and are not comprehensive evidence of quality or safety.[1]
Where to use it
- Evaluate for document- or file-heavy professional workflows where long-context analysis and text responses are required.[1]
- Consider testing for tool-using coding, app-building, data-visualization, or game-development assistants, using your own acceptance checks.[1]
- Potentially suitable for structured content-generation pipelines that need controlled formatting and adjustable reasoning effort.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5.5Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...
Specifications
- Model ID
- openai/gpt-5.5
- Canonical ID
- openai/gpt-5.5-20260423
- Creator
- openai
- Added to OpenRouter
- 24.04.2026
- 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 $5 / out $30
- Knowledge cutoff
- 01.12.2025
- 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: optional · default medium · efforts: xhigh, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | SS — top 10% of the cohort | — |
| Agents | AA — next 20% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default medium · efforts: xhigh, high, medium, low, none · 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 | Coding index | 74.9 | SS — top 10% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 36.4 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 52.3% · rank 14 | 1,127Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agentichtmlslideswin rate 34.2% · rank 8 | 1,084Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslideswin rate 43.5% · rank 6 | 1,150Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(html)win rate 33.2% · rank 8 | 1,077Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(python-pptx)win rate 45.2% · rank 6 | 1,155Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 50.8% · rank 26 | 1,153Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 43% · rank 33 | 1,067Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 52.3% · rank 18 | 1,155Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 35.6% · rank 24 | 963Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 50.4% · rank 34 | 1,136Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · pptxslideswin rate 45.3% · rank 6 | 1,157Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 43.3% · rank 20 | 1,152Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 42.6% · rank 38 | 1,099Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 48.9% · rank 58 | 1,178Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 57.4% · rank 19 | 1,247Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 52.9% · rank 43 | 1,250Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 55.4% · rank 30 | 1,260Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 56.3% · rank 22 | 1,283Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 54.9% · rank 20 | 1,227Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 53.2% · rank 43 | 1,250Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 52.2% · rank 44 | 1,249Elo | BB — next 30% 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 | $2.5 | $15 | — | — | — |
| Azure | 1,050,000 | $5 | $30 | — | — | 100% |
| OpenAI | 1,050,000 | $5 | $30 | — | — | 100% |
| Azure | 1,050,000 | $5.5 | $33 | — | — | — |
| Azure | 1,050,000 | $5.5 | $33 | — | — | — |
| Amazon Bedrock | 1,050,000 | $5.5 | $33 | — | — | — |
| OpenAI | 1,050,000 | $12.5 | $75 | — | — | 100% |
Provider data retrieved: 13.11.2026