Google: Gemini 3.6 Flash (batch)
google/gemini-3.6-flash:batch
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 google/gemini-3.6-flash:batch as a Google multimodal text-output model intended for efficient coding, agentic workflows, and web or app development. The listing reports text, image, file, audio, and video inputs; a very large context window; mandatory reasoning with selectable effort; and support for tools and structured outputs. It also includes arena and index measurements, but this source pack contains no independent model card, so capabilities and real-world fit remain catalog-reported and should be validated on target workloads.[1]
Strengths
- Accepts text, image, file, audio, and video inputs while returning text.[1]
- Offers a very large context window plus controls for tools, structured outputs, response format, seed, stop, and token limits.[1]
- Reasoning is mandatory and enabled by default at medium effort, with minimal through high options.[1]
Limitations
- Only OpenRouter catalog evidence was supplied; an independent model card was not available in this source pack.[1]
- License terms, knowledge cutoff, hardware needs, safety properties, and execution behavior specific to the :batch variant are not reported.[1]
- The benchmark data lacks an intelligence-index value and does not prove suitability for any particular workload.[1]
Where to use it
- Evaluate for coding assistants, agentic workflows, and web or app development, in line with the catalog's stated positioning.[1]
- Consider for multimodal applications that take files, images, audio, or video and produce text.[1]
- Try where tool calling, structured outputs, or adjustable reasoning effort are important integration controls.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini 3.6 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 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...
Specifications
- Model ID
- google/gemini-3.6-flash:batch
- Canonical ID
- google/gemini-3.6-flash-20260721
- Creator
- Added to OpenRouter
- 21.07.2026
- 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, minimal · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | AA — next 20% of the cohort | — |
| Agents | BB — next 30% 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, 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 | input: audio |
| Video | No measurement | input: video |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 69.2 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 29 | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 47.3% · rank 16 | 1,125Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 53.3% · rank 16 | 1,193Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 49.6% · rank 13 | 1,098Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 42% · rank 19 | 1,106Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 44% · rank 23 | 1,159Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 41.9% · rank 19 | 1,045Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 49.6% · rank 21 | 1,192Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 49% · rank 19 | 1,179Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 39.3% · rank 26 | 1,102Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 50.6% · rank 15 | 1,166Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 45.2% · rank 27 | 1,178Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 52.3% · rank 30 | 1,253Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 57.4% · rank 8 | 1,277Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 52.7% · rank 23 | 1,280Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 52.3% · rank 15 | 1,288Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 50.4% · rank 35 | 1,253Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 53.8% · rank 21 | 1,291Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 54.3% · rank 18 | 1,293Elo | 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.