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Gemini 2.5 Pro Preview 06-05 vs Mistral Large 2

Gemini 2.5 Pro Preview 06-05 (2025) and Mistral Large 2 (2025) are compact production models from Google DeepMind and MistralAI. Gemini 2.5 Pro Preview 06-05 ships a 1M-token context window, while Mistral Large 2 ships a 128K-token context window. On pricing, Mistral Large 2 costs $0.48/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Mistral Large 2 is ~160% cheaper at $0.48/1M; pay for Gemini 2.5 Pro Preview 06-05 only for coding workflow support.

Specs

Released2025-03-252025-11-25
Context window1M128K
Parameters123B
Architecturedecoder onlydecoder only
LicenseUnknownTrue
Knowledge cutoff-2025-07

Pricing and availability

Gemini 2.5 Pro Preview 06-05Mistral Large 2
Input price$1.25/1M tokens$0.48/1M tokens
Output price$10/1M tokens$2.4/1M tokens
Providers

Capabilities

Gemini 2.5 Pro Preview 06-05Mistral Large 2
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Mistral Large 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, tool use: Mistral Large 2, and code execution: Gemini 2.5 Pro Preview 06-05. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Gemini 2.5 Pro Preview 06-05 lists $1.25/1M input and $10/1M output tokens, while Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 2 lower by about $2.82 per million blended tokens. Availability is 1 providers versus 4, so concentration risk also matters.

Choose Gemini 2.5 Pro Preview 06-05 when coding workflow support and larger context windows are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.

FAQ

Which has a larger context window, Gemini 2.5 Pro Preview 06-05 or Mistral Large 2?

Gemini 2.5 Pro Preview 06-05 supports 1M tokens, while Mistral Large 2 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Gemini 2.5 Pro Preview 06-05 or Mistral Large 2?

Mistral Large 2 is cheaper on tracked token pricing. Gemini 2.5 Pro Preview 06-05 costs $1.25/1M input and $10/1M output tokens. Mistral Large 2 costs $0.48/1M input and $2.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemini 2.5 Pro Preview 06-05 or Mistral Large 2 open source?

Gemini 2.5 Pro Preview 06-05 is listed under Unknown. Mistral Large 2 is listed under True. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, Gemini 2.5 Pro Preview 06-05 or Mistral Large 2?

Mistral Large 2 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for multimodal input, Gemini 2.5 Pro Preview 06-05 or Mistral Large 2?

Mistral Large 2 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Gemini 2.5 Pro Preview 06-05 and Mistral Large 2?

Gemini 2.5 Pro Preview 06-05 is available on OpenRouter. Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.