openai
OpenAI: GPT-5
openai/gpt-5
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 as a text-output model accepting text, image, and file inputs, with a large context window and mandatory reasoning across several effort settings. The catalog also lists tool use, structured outputs, response formatting, seeds, and verbosity controls. Its description emphasizes complex tasks involving step-by-step reasoning, code quality, and instruction following. It reports a September 2024 knowledge cutoff and a December 2026 catalog expiration. Evidence is catalog-only; no independent model card was available in this source pack.[1]
Strengths
Limitations
- Evidence is OpenRouter catalog-only and measured results cover selected design and coding categories; no independent model card was supplied, so workload validation is needed.[1]
- Only text output is listed, and mandatory reasoning means no disabled mode is shown.[1]
- The reported September 2024 knowledge cutoff and December 2026 expiration make freshness and continued availability important checks.[1]
Where to use it
- Potentially suitable for complex multistep instruction-following and reasoning workflows, subject to validation.[1]
- Potentially suitable for coding assistance that uses reasoning controls, tool calls, or structured outputs.[1]
- Potentially suitable for producing text from mixed text, image, and file inputs.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT-5Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy...
Specifications
- Model ID
- openai/gpt-5
- Canonical ID
- openai/gpt-5-2025-08-07
- Creator
- openai
- Added to OpenRouter
- 07.08.2025
- Context length
- 400,000
- Modality
- text+image+file->text
- Input modalities
- text, image, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $1.25 / out $10
- Knowledge cutoff
- 30.09.2024
- Expiration date
- 11.12.2026
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, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | CC — below the B band | — |
| 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, minimal · 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 | 37.8 | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 41.3% · rank 113 | 1,038Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · asciiartwin rate 49% · rank 51 | 1,136Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 54.5% · rank 82 | 1,158Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 60.5% · rank 52 | 1,219Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 59.3% · rank 66 | 1,176Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 64.1% · rank 38 | 1,180Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 57.4% · rank 72 | 1,172Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 53.7% · rank 79 | 1,177Elo | 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) |
|---|---|---|---|---|---|---|
| Azure | 400,000 | $1.25 | $10 | — | — | 100% |
| OpenAI | 400,000 | $1.25 | $10 | — | — | 100% |
| Azure | 400,000 | $1.38 | $11 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
- OpenAI study links fading chain-of-thought transparency to capability and compute trade-offsAn analysis by OpenAI's safety team attributes the declining monitorability of frontier models like GPT-6 Astra to four training factors, framing the loss of visible reasoning as a trade-off between safety, capability, and cost.
- Langfuse v4.79.0 brings OpenRouter, xAI and Vercel AI Gateway providers to general availabilityLangfuse v4.79.0 graduates three new AI Gateway providers, expands MCP and evaluation tooling, adds audit-log coverage for self-hosted enterprise installs, and ships a round of fixes and dependency updates.