---
title: "Gemini 3.7 Flash AI model: facts and benchmarks | Kendr"
canonical: "https://kendr.org/models/google-gemini-3-7-flash"
date_modified: "2026-09-06"
profile_kind: "reference"
---

# Gemini 3.7 Flash model profile

Gemini 3.7 Flash is a Google model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video, file, audio modalities and interfaces for audio input, file input, prompt caching, reasoning controls, structured output.

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

Gemini 3.7 Flash is a reference profile: Google publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Gemini 3.7 Flash is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.

Gemini 3.7 Flash accepts text, image, video, file, and audio and returns text. The snapshot records audio input, file input, prompt caching, reasoning controls, structured output, text generation, tool calling, video input, vision, and web search pricing as capabilities or interfaces.

The dated reference snapshot lists $0.375 input and $1.875 output per million tokens. Cached input is $0.0375 per million tokens.

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 11 Design Arena categories, and AutoExacto results for 3 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Gemini 3.7 Flash ranked #12 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

## Profile facts

- **Profile type:** Model research profile
- **Provider or publisher:** Google
- **Context window:** 1,048,576 tokens
- **Snapshot date:** 2026-09-06
- **Knowledge cutoff:** Not disclosed
- **Reference catalog ID:** google/gemini-3.7-flash

## When to pick Gemini 3.7 Flash

Gemini 3.7 Flash is a reference profile: Google publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

- Reach for it when a whole corpus has to fit in one prompt. The 1.05M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked.
- Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
- Reach for it when the input is speech. The snapshot records audio input, so recordings can go to this route rather than being transcribed first.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the request is trivial and latency-sensitive. Reasoning is mandatory on this route and cannot be turned off, so short factual requests still pay reasoning tokens and reasoning latency.

## Context, modalities, and identity

Gemini 3.7 Flash accepts text, image, video, file, and audio and returns text. The snapshot records audio input, file input, prompt caching, reasoning controls, structured output, text generation, tool calling, video input, vision, and web search pricing as capabilities or interfaces.

- **Provider or publisher:** Google
- **Context window:** 1,048,576 tokens
- **Maximum output:** 65,536 tokens
- **Input modalities:** text, image, video, file, and audio
- **Output modalities:** text
- **Knowledge cutoff:** Not disclosed

## Model overview

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

- **Reference model ID:** google/gemini-3.7-flash
- **Canonical version:** google/gemini-3.7-flash-20260813
- **Hugging Face ID:** Not separately documented

## Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

- **Supported parameters:** include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p

- audio input
- file input
- prompt caching
- reasoning controls
- structured output
- text generation
- tool calling
- video input
- vision
- web search pricing

## Dated pricing snapshot

The dated reference snapshot lists $0.375 input and $1.875 output per million tokens. Cached input is $0.0375 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

- **Price date:** 2026-09-06
- **Input:** $0.375 per 1M tokens
- **Cached input:** $0.0375 per 1M tokens
- **Output:** $1.875 per 1M tokens

## Provider routes, performance, and uptime

The snapshot retains 6 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

- **Active provider endpoints:** 5
- **Best p50 latency:** 1.07 s
- **Best p50 throughput:** 203 tok/s
- **Availability with routing:** 99.86% over the sampled window
- **Availability without routing:** 97.64% over the sampled window
- **Performance date:** 2026-09-06

| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Google AI Studio Flex | unknown | $0.375 / 1M | $1.875 / 1M | $0.0375 / 1M | 1,048,576 tokens | 1.07 s | 189 tok/s | 98.83% |
| Google AI Studio | unknown | $0.75 / 1M | $3.75 / 1M | $0.075 / 1M | 1,048,576 tokens | 1.32 s | 203 tok/s | 98.63% |
| Google Vertex | unknown | $0.75 / 1M | $3.75 / 1M | $0.075 / 1M | 1,048,576 tokens | 2.30 s | 87 tok/s | 99.27% |
| Google Vertex Priority | unknown | $1.35 / 1M | $6.75 / 1M | $0.135 / 1M | 1,048,576 tokens | 2.50 s | 84 tok/s | 99.86% |
| Google AI Studio Priority | unknown | $1.35 / 1M | $6.75 / 1M | $0.135 / 1M | 1,048,576 tokens | 1.09 s | 180 tok/s | 99.69% |
| Google Vertex Flex | unknown | $0.375 / 1M | $1.875 / 1M | $0.0375 / 1M | 1,048,576 tokens | 14.09 s | 47 tok/s | 40.26% |

## Benchmark evidence and limitations

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 11 Design Arena categories, and AutoExacto results for 3 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

- **Intelligence Index:** 45.2
- **Coding Index:** 76.1
- **Agentic Index:** 36.6
- **AutoExacto coverage:** 3 provider observations over 32 days

| Design Arena category | Elo | Win rate | Rank |
| --- | --- | --- | --- |
| agenticgamedev | 1235 | 52.1% | #5 |
| androidnative | 1263 | 53.9% | #5 |
| fullstack | 1201 | 44.8% | #16 |
| mobileapps | 1263 | 53.2% | #6 |
| webapps | 1243 | 48.8% | #11 |
| 3d | 1354 | 62.9% | #7 |
| codecategories | 1320 | 57.5% | #7 |
| dataviz | 1325 | 58.9% | #7 |
| gamedev | 1346 | 58.8% | #7 |
| uicomponent | 1303 | 53.5% | #13 |
| website | 1314 | 57.1% | #7 |

## AutoExacto provider benchmarks

Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.

| Provider | GPQA Diamond | TAU-Bench Airline | Runs |
| --- | --- | --- | --- |
| auto-routing | 94.28% | — | 1 |
| Google AI Studio | 94.5% | 80.67% | 2 |
| Google Vertex | 93.73% | 80.56% | 2 |

## Popularity and market context

Gemini 3.7 Flash ranked #12 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

- **Third-party catalog rank:** #12
- **Global market share:** Not inferred
- **Observation date:** 2026-09-06

## Frequently asked questions

### When should I use Gemini 3.7 Flash?

Gemini 3.7 Flash is a reference profile: Google publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when a whole corpus has to fit in one prompt: The 1.05M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the input is speech: The snapshot records audio input, so recordings can go to this route rather than being transcribed first. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the request is trivial and latency-sensitive: Reasoning is mandatory on this route and cannot be turned off, so short factual requests still pay reasoning tokens and reasoning latency.

### What is Gemini 3.7 Flash?

Gemini 3.7 Flash is a Google model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video, file, audio modalities and interfaces for audio input, file input, prompt caching, reasoning controls, structured output.

### What context window does Gemini 3.7 Flash have?

The dated profile lists 1,048,576 tokens of context and up to 65,536 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

### What benchmark evidence is available for Gemini 3.7 Flash?

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 11 Design Arena categories, and AutoExacto results for 3 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

### Is Gemini 3.7 Flash available through Kendr?

This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.

### Does the popularity rank represent Gemini 3.7 Flash's global market share?

Gemini 3.7 Flash ranked #12 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share. No global market-share percentage is inferred when the source does not publish one.

## Sources and evidence dates

- [Gemini 3.7 Flash third-party catalog record](https://openrouter.ai/google/gemini-3.7-flash) — catalog, checked 2026-09-06
- [Third-party model catalog methodology](https://openrouter.ai/docs/guides/overview/models) — methodology, checked 2026-09-06
- [Google official model documentation](https://ai.google.dev/gemini-api/docs/models) — primary, checked 2026-09-06
- [Artificial Analysis capability indices methodology](https://artificialanalysis.ai/methodology/capability-indices) — benchmark, checked 2026-09-06
- [Design Arena leaderboard and methodology](https://www.designarena.ai/leaderboard) — benchmark, checked 2026-09-06
- [Gemini 3.7 Flash third-party catalog record](https://openrouter.ai/blog/announcements/auto-exacto/) — benchmark, checked 2026-09-06

Live Kendr operational availability is published separately at https://api.kendr.org/api/public/models.