Google, proprietary

# Gemini 3.7 Flash

> Gemini 3.7 Flash by Google, released August 2026. Ranked #14 of 354 with a Noometry Index of 59.8. API: $0.75 in / $3.75 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gemini-3-7-flash
- Last updated: 2026-10-10
- Title: Gemini 3.7 Flash Benchmarks, Price & Rank (October 2026)

Gemini 3.7 Flash by Google ranks 14th of 354 ranked models on the Noometry Index as of October 2026, with a score of 59.8. Its strongest category is knowledge, where it ranks 5th. API pricing starts at $0.75 per million input tokens and $3.75 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #14 of 354
- **Index score:** 59.8
- **Evidence:** Confirmed 44 results
- **Provider:** [![](/logos/google.svg) Google](https://noometry.com/providers/google)
- **Released:** August 13, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 66K
- **Input price:** $0.75 / M
- **Output price:** $3.75 / M
- **Blended price:** $1.50 / M
- **Output speed:** Not measured
- **Value:** #119 of 219
- **Knowledge cutoff:** March 2026
- **Input:** text, image, video, audio, pdf

## Category scores

Each category score combines every public result we have in that category.

Gemini 3.7 Flash category scores

1.  Coding 56.2
2.  Agentic & Tool Use 42.1
3.  Reasoning 70.0
4.  Math 69.6
5.  Knowledge 69.7
6.  Multimodal 37.3
7.  Multilingual 57.6
8.  Instruction Following 77.7
9.  Long Context 45.7
10.  Writing & Preference 71.2
11.  020406080

Gemini 3.7 Flash category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 56.2 | #22 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 42.1 | #19 | 3 |
| [Reasoning](https://noometry.com/best/reasoning) | 70.0 | #15 | 9 |
| [Math](https://noometry.com/best/math) | 69.6 | #23 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 69.7 | #5 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 37.3 | #73 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 57.6 | #7 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.7 | #15 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.7 | #30 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 71.2 | #20 | 4 |

## Strengths and weaknesses

Categories where Gemini 3.7 Flash places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Gemini 3.7 Flash: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 69.7 | +32.4 | #5 of 314, top 2% |
| [Multilingual](https://noometry.com/best/multilingual) | 57.6 | +10.2 | #7 of 297, top 3% |
| [Reasoning](https://noometry.com/best/reasoning) | 70.0 | +46.4 | #15 of 350, top 5% |

### Weakest categories

Gemini 3.7 Flash: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 37.3 | −1.2 | #73 of 128, top 58% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 42.1 | +11.7 | #19 of 154, top 13% |
| [Long Context](https://noometry.com/best/long-context) | 45.7 | +4.8 | #30 of 296, top 11% |

## Closest competitors

The models ranked just above and below Gemini 3.7 Flash. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Gemini 3.7 Flash
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | #10 | 61.9 | $4 | — | [Compare](https://noometry.com/compare/claude-sonnet-5-5-vs-gemini-3-7-flash) |
| [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | #11 | 61.8 | $1.50 | — | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gemini-3-8-flash) |
| [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | #12 | 61.8 | $4 | — | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-6-sol) |
| [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | #13 | 60.7 | $10 | 34 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-7-flash) |
| [Kimi K3](https://noometry.com/models/kimi-k3) | #15 | 59.5 | $6 | — | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-kimi-k3) |
| [GPT-5.4](https://noometry.com/models/gpt-5-4) | #16 | 59.4 | $5.63 | 12 | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-4) |
| [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | #17 | 59.2 | $4.50 | 11 | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-6-terra) |
| [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | #18 | 58.9 | $67.50 | — | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-4-pro) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Gemini 3.7 Flash Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 65.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 53.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 65.5% | #15 of 29, top 52% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 43.6% | #14 of 37, top 38% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1592 | #24 of 113, top 22% | high | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 20.3% | #13 of 18, top 73% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 53.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 59.8% | #7 of 121, top 6% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1497 | #23 of 294, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 904.3 | #50 of 105, top 48% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Gemini 3.7 Flash Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 67.8% | #5 of 49, top 11% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 5% | #7 of 14, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 23.8% | #13 of 36, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 21.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Gemini 3.7 Flash Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 84.6% | #11 of 83, top 14% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 52.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 63.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 94% | #8 of 91, top 9% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 95.5% | #13 of 83, top 16% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 85.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 91.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 14.3% | #39 of 134, top 30% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 5.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 9.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 47% | #14 of 129, top 11% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1494 | #14 of 297, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 37% | #15 of 74, top 21% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 96.8% | #9 of 151, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 93.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 50.4% | #20 of 125, top 16% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 47.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 49.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 157.27 | #16 of 213, top 8% |  | [Epoch AI](https://epoch.ai/eci) | 2026-08-13 |

### Math

Gemini 3.7 Flash Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 71.6% | #23 of 81, top 29% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 36.6% | #24 of 63, top 39% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 97.2% | #27 of 173, top 16% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 58% | #20 of 77, top 26% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1507 | #8 of 285, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Gemini 3.7 Flash Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 94.8% | #5 of 186, top 3% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-14 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 69.2% | #9 of 77, top 12% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1508 | #16 of 273, top 6% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Gemini 3.7 Flash Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1316 | #6 of 122, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 26.7% | #26 of 31, top 84% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |

### Multilingual

Gemini 3.7 Flash Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1484 | #7 of 297, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1548 | #7 of 285, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1505 | #7 of 223, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1498 | #7 of 231, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1512 | #3 of 211, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1483 | #5 of 213, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1516 | #4 of 283, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1503 | #5 of 226, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Gemini 3.7 Flash Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1483 | #12 of 298, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Gemini 3.7 Flash Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1492 | #12 of 291, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Gemini 3.7 Flash Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1486 | #11 of 297, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1490 | #8 of 295, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1723 | #29 of 115, top 26% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1489 | #10 of 295, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Gemini 3.7 Flash API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [google](https://ai.google.dev/gemini-api/docs/models) | $0.75 | $3.75 | $0.075 | 2026-10-10 |
| [openrouter](https://openrouter.ai/google/gemini-3.7-flash) | $0.75 | $3.75 | $0.075 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $0.75 | $3.75 | $0.075 | 2026-10-10 |

[All Google API prices →](https://noometry.com/llm-pricing/google) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Gemini 3.7 Flash

-   [Gemini 3.7 Flash vs Gemini 3.6 Flash](https://noometry.com/compare/gemini-3-6-flash-vs-gemini-3-7-flash)
-   [Gemini 3.7 Flash vs Claude Opus 4.8](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-7-flash)
-   [Gemini 3.7 Flash vs Kimi K3](https://noometry.com/compare/gemini-3-7-flash-vs-kimi-k3)
-   [Gemini 3.7 Flash vs GPT-6 Sol](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-6-sol)
-   [Gemini 3.7 Flash vs GPT-5.4](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-4)
-   [Gemini 3.7 Flash vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-7-flash-vs-gemini-3-8-flash)
-   [Gemini 3.7 Flash vs GPT-5.6 Terra](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-6-terra)
-   [Gemini 3.7 Flash vs GPT-6 Astra](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-6-astra)
-   [Gemini 3.7 Flash vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gemini-3-7-flash)
-   [Gemini 3.7 Flash vs Grok 4.6](https://noometry.com/compare/gemini-3-7-flash-vs-grok-4-6)
-   [Gemini 3.7 Flash vs Qwen3.8 Max](https://noometry.com/compare/gemini-3-7-flash-vs-qwen3-8-max)
-   [Gemini 3.7 Flash vs GLM-5.3](https://noometry.com/compare/gemini-3-7-flash-vs-glm-5-3)
-   [Gemini 3.7 Flash vs Muse Spark 1.3](https://noometry.com/compare/gemini-3-7-flash-vs-muse-spark-1-3)
-   [Gemini 3.7 Flash vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gemini-3-7-flash)

## Other Google models

-   [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash)61.8
-   [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview)56.7
-   [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon)56.5
-   [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro)54.8
-   [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash)54.2
-   [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash)54.1
-   [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview)52.3
-   [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro)45.0

## Frequently asked questions

### How good is Gemini 3.7 Flash?

Gemini 3.7 Flash by Google ranks 14th of 354 ranked models on the Noometry Index as of October 2026, with a score of 59.8. Its strongest category is knowledge, where it ranks 5th. API pricing starts at $0.75 per million input tokens and $3.75 per million output tokens, with a 1.05M-token context window.

### How much does Gemini 3.7 Flash cost?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens on Google's own API, with cached input at $0.075.

### What is Gemini 3.7 Flash's context window?

Gemini 3.7 Flash accepts up to 1.05M tokens of input and can write up to 66K tokens in one response.

### Is Gemini 3.7 Flash open source?

No. Gemini 3.7 Flash is proprietary and available only through Google's API and partner platforms.

### What are Gemini 3.7 Flash's strengths and weaknesses?

Relative to other ranked models, Gemini 3.7 Flash places best in knowledge, multilingual, reasoning and lowest in multimodal, agentic & tool use, long context.

### What is Gemini 3.7 Flash best at?

Its best category is knowledge, where it ranks 5th on Noometry.

### Cite this page

Noometry. (2026). Gemini 3.7 Flash benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gemini-3-7-flash

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gemini-3-7-flash.md).