CrossModel
Gemini 3.5 Flash
gemini/gemini-3.5-flash
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Gemini 3.5 Flash

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gemini/gemini-3.5-flash

A stable native-multimodal Gemini 3 reasoning model with controllable thinking and a 1M-token context. It sustains high performance across coding, tool use, computer-oriented workflows and multimodal knowledge work.

Modalities
TextImageAudioVideoText
Context
1M
Max output
66K
Price / 1M tokens
$ 1.50in$ 9.00out
StreamingTool callingJSON outputImage inputThinking mode · can be turned off

What this model is good at

What the vendor positions it for, and which of our models to reach for instead.

VendorThe most intelligent stable Gemini Flash model for sustained agentic and coding performance at launch.source

Best for

  • Long-running coding agents · Strong terminal, repository and tool-use results suit iterative coding loops.(vendor claim)source

  • Complex multimodal reasoning · It natively processes image, audio and video with controllable reasoning depth.source

Neighbouring models
Compare them side by side →

Pricing and billing

Billed per token. Cached input is charged at the cache-read rate.

Price / 1M tokensYou pay
Input$ 1.50
Output$ 9.00
Cache read$ 0.15
Cache write$ 1.50
Estimate a request
$ 0.0330
Estimated cost per request
10.0K × $ 1.50
+ 2.0K × $ 9.00
≈ $ 33.00 per 1,000 requests

Capabilities and limits

What CrossModel guarantees across every route this model can take right now.

Context window
1.0M tokens
Max output
65.5K tokens
Input / output modalities
Text + Image + Audio + Video → Text
Streaming
Supported
Tool calling
Supported
Structured output (JSON)
Supported
Image input
Supported
Thinking mode
Supported· can be turned off
Reasoning effort
minimal · low · medium · high
Available endpoints
/v1/chat/completions · /v1/responses · /v1/messages
First-party sources:Specsdeepmind.googleCapabilitiesai.google.dev 1ai.google.dev 2

Published benchmarks

Scores the vendor reported, with the evaluation setup each one came from.

Agentic coding
  • Terminal-Bench 2.1
    pass_rate·Google DeepMind · 2026-05-19Terminus 2setup not fully disclosed
    76.2%
Coding
  • SWE-Bench Pro (Public)
    pass_rate·Google DeepMind · 2026-05-19setup not fully disclosed

    Single attempt.

    55.1%
Computer use
  • OSWorld-Verified
    success_rate·Google DeepMind · 2026-05-19setup not fully disclosed
    78.4%
Multimodal reasoning
  • MMMU Pro (no tools)
    accuracy·Google DeepMind · 2026-05-19setup not fully disclosed
    83.6%
Tool use
  • MCP Atlas Public
    score·Google DeepMind · 2026-05-19setup not fully disclosed
    83.6%

Vendor-reported numbers, not CrossModel measurements. Scores are only comparable when the benchmark version, metric and evaluation setup match, so nothing here is averaged or ranked.

Use it in your tools

Point the base URL at CrossModel and paste this model ID — every tool below has a setup guide.

Claude Code
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Cline
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Codex
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OpenCode
Open-source terminal coding assistant
Zed
High-performance, AI-native code editor
Open WebUI
Open-source, self-hosted AI chat interface
Continue
Open-source AI coding assistant for VS Code & JetBrains
See all integration guides →

Frequently asked questions

What is Gemini 3.5 Flash?
Gemini 3.5 Flash is available on CrossModel as gemini/gemini-3.5-flash. It has a 1M-token context window and can return up to 66K tokens per request.
How much does Gemini 3.5 Flash cost?
Currently $ 1.50 per 1M input tokens and $ 9.00 per 1M output tokens. Cached input is billed at $ 0.15 per 1M.
Does Gemini 3.5 Flash support tool calling and structured output?
Tool calling: Supported · Structured output (JSON): Supported · Image input: Supported · Streaming: Supported · Thinking mode: Supported
Which endpoint do I call?
For text models, OpenAI Chat Completions (/v1/chat/completions), OpenAI Responses (/v1/responses) and Anthropic Messages (/v1/messages) all work — pick whichever your SDK already speaks.
Can I try it without writing code?
Yes. Sign in and open the Playground on this page to chat with the model directly — no API key needed. Usage is billed from your wallet at the normal rate.

Chat first, integrate later

Chat first, then wire it in once you like the answers.