# Gemini 3.7 Flash Usage, Cost & Rank | OpenCode Data

> Gemini 3.7 Flash had 0% of tokens across OpenCode over the past two months. Gemini 3.7 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens.

High-efficiency Gemini model for agentic workflows, coding, and multimodal reasoning

- Page: https://opencode.ai/data/google/gemini-3-7-flash
- JSON: https://opencode.ai/data/google/gemini-3-7-flash.json
- Model ID: google/gemini-3.7-flash
- Lab: Google
- Updated: 2026-09-28T12:09:39.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 66K |
| Knowledge cutoff | 2026-03 |
| Release date | 2026-08-13 |
| Input modalities | text, image, video, audio, pdf |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.75 | $3.75 | $0.07 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | Unranked |
| Tokens | 20 |
| Share of all tokens | 0% |
| Change vs previous 2 months | +100% |
| Unique users | 1 |
| Completed sessions | 2 |
| Average tokens per session | 10 |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 0% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-13 | 20 | 1 | 1 |
| 2026-08-14 | 0 | 0 | 0 |
| 2026-08-15 | 0 | 0 | 0 |
| 2026-08-16 | 0 | 0 | 0 |
| 2026-08-17 | 0 | 0 | 0 |
| 2026-08-18 | 0 | 0 | 0 |
| 2026-08-19 | 0 | 0 | 0 |
| 2026-08-20 | 0 | 0 | 0 |
| 2026-08-21 | 0 | 0 | 0 |
| 2026-08-22 | 0 | 0 | 0 |
| 2026-08-23 | 0 | 0 | 0 |
| 2026-08-24 | 0 | 0 | 0 |
| 2026-08-25 | 0 | 0 | 0 |
| 2026-08-26 | 0 | 0 | 0 |
| 2026-08-27 | 0 | 0 | 0 |
| 2026-08-28 | 0 | 0 | 0 |
| 2026-08-29 | 0 | 0 | 0 |
| 2026-08-30 | 0 | 0 | 0 |
| 2026-08-31 | 0 | 0 | 0 |
| 2026-09-01 | 0 | 0 | 0 |
| 2026-09-02 | 0 | 0 | 0 |
| 2026-09-03 | 0 | 0 | 0 |
| 2026-09-04 | 0 | 0 | 0 |
| 2026-09-05 | 0 | 0 | 0 |
| 2026-09-06 | 0 | 0 | 0 |
| 2026-09-07 | 0 | 0 | 0 |
| 2026-09-08 | 0 | 0 | 0 |
| 2026-09-09 | 0 | 0 | 0 |
| 2026-09-10 | 0 | 0 | 0 |
| 2026-09-11 | 0 | 0 | 0 |
| 2026-09-12 | 0 | 0 | 0 |
| 2026-09-13 | 0 | 0 | 0 |
| 2026-09-14 | 0 | 0 | 0 |
| 2026-09-15 | 0 | 0 | 0 |
| 2026-09-16 | 0 | 0 | 0 |
| 2026-09-17 | 0 | 0 | 0 |
| 2026-09-18 | 0 | 0 | 0 |
| 2026-09-19 | 0 | 0 | 1 |
| 2026-09-20 | 0 | 0 | 0 |
| 2026-09-21 | 0 | 0 | 0 |
| 2026-09-22 | 0 | 0 | 0 |
| 2026-09-23 | 0 | 0 | 0 |
| 2026-09-24 | 0 | 0 | 0 |
| 2026-09-25 | 0 | 0 | 0 |
| 2026-09-26 | 0 | 0 | 0 |
| 2026-09-27 | 0 | 0 | 0 |
| 2026-09-28 | 0 | 0 | 0 |
| 2026-09-29 | 0 | 0 | 0 |
| 2025-09-30 | 0 | 0 | 0 |
| 2025-10-01 | 0 | 0 | 0 |
| 2025-10-02 | 0 | 0 | 0 |
| 2025-10-03 | 0 | 0 | 0 |
| 2025-10-04 | 0 | 0 | 0 |
| 2025-10-05 | 0 | 0 | 0 |
| 2025-10-06 | 0 | 0 | 0 |
| 2025-10-07 | 0 | 0 | 0 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 0 | 100% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 1 | [space-bunny](https://opencode.ai/data/unknown/space-bunny.md) | - | 53T |
| 2 | [muse-spark-1.3-contributor](https://opencode.ai/data/meta/muse-spark-1-3-contributor.md) | Meta | 33T |
| 3 | [deepseek-v4.1-flash](https://opencode.ai/data/deepseek/deepseek-v4-1-flash.md) | DeepSeek | 31T |
| 4 | [mimo-v2.6-flash](https://opencode.ai/data/xiaomi/mimo-v2-6-flash.md) | Xiaomi | 9.9T |
| 5 | [deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-v4-flash.md) | DeepSeek | 3.8T |
| 6 | [nemotron-3-ultra](https://opencode.ai/data/nvidia/nemotron-3-ultra.md) | NVIDIA | 3.6T |
| 7 | [longcat-2.5-preview](https://opencode.ai/data/meituan/longcat-2-5-preview.md) | Meituan | 2.1T |
| 8 | [fledge-alpha](https://opencode.ai/data/unknown/fledge-alpha.md) | - | 1.7T |
| 9 | [glm-5.3-flash](https://opencode.ai/data/zhipuai/glm-5-3-flash.md) | Zhipu | 1.7T |
| 10 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 653B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| FrontierCode | 43.6 | score | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |
| DeepSWE | 65.3 | resolve rate | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |
| Terminal-Bench | 85.8 | accuracy | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |
| AutomationBench | 30.4 | accuracy | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |
| GDP.pdf | 34 | accuracy | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |
| GDM-MRCR | 97 | accuracy | https://deepmind.google/models/model-cards/gemini-3-7-flash/ |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.