Google: Gemini 3.5 Flash (batch)
google/gemini-3.5-flash:batch
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:batch
- 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 $0.750 / out $4.5
- 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 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.