Google: Gemini 3.7 Flash (batch)
google/gemini-3.7-flash:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to OpenRouter’s catalog, google/gemini-3.7-flash:batch is a Google multimodal text-generation model intended for fast agentic workflows, coding, and complex multi-step reasoning. It accepts text, image, file, audio, and video inputs, produces text, and lists a 1,048,576-token context window. Reasoning is mandatory and enabled by default, with low, medium, and high effort settings. The catalog reports benchmark coverage for coding, agentic, and design tasks; an independent model card was not available in this source pack.[1]
Strengths
Limitations
- Evidence is limited to the OpenRouter catalog; no independent model card, license terms, knowledge cutoff, deployment requirements, or safety evaluation details were supplied. Validate on representative workloads.[1]
- Reasoning is mandatory, which may not suit configurations requiring direct generation without a reasoning mode.[1]
- Despite accepting several input modalities, it generates only text.[1]
Where to use it
- The catalog-labeled batch variant could be considered for high-volume jobs that produce text from mixed inputs; confirm provider behavior before adoption.[1]
- Potentially suitable for tool-using coding or agent workflows that benefit from adjustable reasoning effort and structured outputs.[1]
- May fit long-context analysis of documents or multimodal content when a text response is sufficient; test with representative data.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini 3.7 Flash (batch)Sources 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:batch
- 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 $0.375 / out $1.88
- 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 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.