Google: Gemini 3.5 Flash
google/gemini-3.5-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 describes google/gemini-3.5-flash as a Google multimodal model that accepts text, image, file, audio, and video inputs and returns text. The catalog reports a very large context window, tool and structured-output support, mandatory reasoning with selectable effort levels, and benchmark entries for agentic design and coding tasks. Its catalog description positions it for efficient coding and parallel agent execution, but this source pack contains no independent model card; production suitability should be validated on the intended workload.[1]
Strengths
- Catalog-listed multimodal input spans text, image, file, audio, and video, with text output.[1]
- The catalog reports a very large context window and tool, structured-output, and response-format controls.[1]
- The catalog publishes arena results for agentic design tasks and a coding index, providing some measured evidence beyond feature claims.[1]
Limitations
- This source pack contains only OpenRouter catalog data and no independent model card, so descriptive claims lack independent corroboration.[1]
- Reasoning is marked as mandatory, although its effort level can be adjusted.[1]
- Outputs are text-only, and the available measured results do not establish performance on a specific production workload.[1]
Where to use it
- Consider for multimodal assistants or document, media, and file understanding when a text response is sufficient.[1]
- Consider for coding or agentic workflows because the catalog emphasizes efficiency and parallel execution; validate with real tasks.[1]
- Consider for applications requiring structured outputs, tool calls, or a large context window, subject to integration testing.[1]
Sources
- [1] OpenRouter catalog · Google: Gemini 3.5 FlashSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...
Specifications
- Model ID
- google/gemini-3.5-flash
- Canonical ID
- google/gemini-3.5-flash-20260519
- Creator
- Added to OpenRouter
- 19.05.2026
- 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 $1.5 / out $9
- Knowledge cutoff
- 01.01.2025
- 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 | 70.1 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 26 | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 55.3% · rank 13 | 1,128Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agentichtmlslideswin rate 45.8% · rank 6 | 1,162Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslideswin rate 57.5% · rank 3 | 1,244Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(html)win rate 45.7% · rank 6 | 1,162Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(python-pptx)win rate 57.8% · rank 2 | 1,242Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 57.3% · rank 21 | 1,174Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 31.5% · rank 24 | 949Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 49% · rank 12 | 1,146Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 55.9% · rank 20 | 1,176Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 43% · rank 31 | 1,081Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 46.4% · rank 21 | 1,038Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 50% · rank 28 | 1,170Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · pdfwin rate 46.1% · rank 26 | 1,138Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · pptxslideswin rate 57.7% · rank 2 | 1,244Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 57.4% · rank 6 | 1,247Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 48.9% · rank 20 | 1,127Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 51.7% · rank 26 | 1,183Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 55.5% · rank 41 | 1,227Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 58.8% · rank 17 | 1,249Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 54% · rank 38 | 1,257Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 53.5% · rank 39 | 1,238Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 52.4% · rank 34 | 1,254Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 58% · rank 18 | 1,246Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 54.6% · rank 35 | 1,266Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 53.8% · rank 35 | 1,261Elo | 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 | $0.750 | $4.5 | — | — | — | |
| Google AI Studio | 1,048,576 | $0.750 | $4.5 | — | — | — |
| 1,048,576 | $1.5 | $9 | — | — | 100% | |
| Google AI Studio | 1,048,576 | $1.5 | $9 | — | — | 100% |
| 1,048,576 | $1.65 | $9.9 | — | — | — | |
| 1,048,576 | $2.7 | $16.2 | — | — | — | |
| Google AI Studio | 1,048,576 | $2.7 | $16.2 | — | — | — |
Provider data retrieved: 13.11.2026