Google: Gemini 3.7 Flash
google/gemini-3.7-flash
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter’s catalog presents google/gemini-3.7-flash as a Google multimodal model for fast agentic workflows, coding, and multi-step reasoning. It accepts text, image, file, audio, and video inputs and returns text. The catalog lists a 1,048,576-token context window, tool calling, structured outputs, and mandatory reasoning with selectable effort. Because this source pack contains only catalog data and no independent model card, the description should be treated as source-reported rather than independently verified.[1]
Strengths
Limitations
- Only OpenRouter catalog evidence was supplied; an independent model card was not available in this source pack.[1]
- Reasoning cannot be disabled, so teams needing a non-reasoning mode should validate fit.[1]
- Catalog-reported benchmark results may not predict performance on a specific workload; validate with representative tests.[1]
Where to use it
- Consider for multimodal assistants that inspect images, files, audio, or video and answer in text.[1]
- Consider for agentic coding or multi-step workflows, as described by the OpenRouter catalog.[1]
- Consider for applications needing tool calls or structured outputs; confirm behavior in integration tests.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini 3.7 FlashSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step...
Specifications
- Model ID
- google/gemini-3.7-flash
- Canonical ID
- google/gemini-3.7-flash-20260813
- Creator
- Added to OpenRouter
- 13.08.2026
- Context length
- 1,048,576
- Modality
- text+image+file+audio+video->text
- Input modalities
- text, image, video, file, audio
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.750 / out $3.75
- 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 | 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: mandatory · default medium · efforts: 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 | input: audio |
| Video | No measurement | input: video |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 76.1 | SS — top 10% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 35.2 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 52.1% · rank 7 | 1,181Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 53.2% · rank 9 | 1,232Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 49.6% · rank 12 | 1,112Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 54.9% · rank 8 | 1,197Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 43.7% · rank 17 | 1,188Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 49.9% · rank 6 | 1,239Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 52.6% · rank 12 | 1,227Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 47.3% · rank 21 | 1,163Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 38% · rank 28 | 1,066Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 46.8% · rank 18 | 1,131Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 47.1% · rank 18 | 1,206Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 53.3% · rank 21 | 1,291Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 53.2% · rank 12 | 1,268Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 53.5% · rank 17 | 1,299Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 54.4% · rank 11 | 1,310Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 53.4% · rank 15 | 1,313Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 54% · rank 12 | 1,280Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 51.4% · rank 22 | 1,288Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 53.6% · rank 16 | 1,296Elo | AA — next 20% 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) |
|---|---|---|---|---|---|---|
| 1,048,576 | $0.375 | $1.88 | — | — | 0% | |
| Google AI Studio | 1,048,576 | $0.375 | $1.88 | — | — | 100% |
| Google AI Studio | 1,048,576 | $0.750 | $3.75 | — | — | 100% |
| 1,048,576 | $0.750 | $3.75 | — | — | 100% | |
| 1,048,576 | $0.825 | $4.13 | — | — | — | |
| 1,048,576 | $1.35 | $6.75 | — | — | 100% | |
| Google AI Studio | 1,048,576 | $1.35 | $6.75 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.