Google: Gemini 3.6 Flash
google/gemini-3.6-flash
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
Gemini 3.6 Flash (google/gemini-3.6-flash) is a Google model that, according to the OpenRouter catalog, targets coding, agentic workflows, and web and app development with an emphasis on efficiency and polished output. It accepts text, image, audio, video, and file inputs and returns text only, within a context window of about one million tokens. Reasoning is always enabled with four selectable effort levels, and the catalog lists tool calling and structured output support. Only OpenRouter catalog data was supplied; no independent model card was available in this source pack, so real-world behavior should be validated on your own workloads.[1]
Strengths
- Broad multimodal input support: text, images, audio, video, and files, with a context window of roughly one million tokens reported by the catalog.[1]
- Configurable reasoning with four effort levels, letting callers trade depth against responsiveness.[1]
- Catalog lists support for tool calling, tool choice, and structured outputs, suiting agentic and application-integration workflows.[1]
Limitations
- Evidence is catalog-only: no independent model card was available in this source pack, and capabilities are vendor-reported rather than independently verified.[1]
- Reasoning is mandatory and cannot be disabled; only its effort level is adjustable, which may add overhead on simple requests.[1]
- Output is text only despite multimodal inputs, and performance evidence is limited to arena-style listings in the catalog; validate on your workloads before adopting.[1]
Where to use it
- Potentially a good fit for coding assistance, agentic pipelines, and web or app development tasks, per the catalog's stated positioning.[1]
- May suit multimodal understanding workflows, such as summarizing documents, images, audio, or video into text.[1]
- Worth evaluating for long-context applications that must ingest large files or extended conversations, subject to workload testing.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini 3.6 FlashSources 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
- 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 $0.750 / 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, 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 | — | — | 100% | |
| 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 | — | — | 100% | |
| 1,048,576 | $1.35 | $6.75 | — | — | — | |
| 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.