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Gemini 2.5 Pro Preview 06-05 vs Kimi K2.5

Gemini 2.5 Pro Preview 06-05 (2025) and Kimi K2.5 (2026) are agentic coding models from Google DeepMind and Moonshot AI. Gemini 2.5 Pro Preview 06-05 ships a 1M-token context window, while Kimi K2.5 ships a 256K-token context window. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Kimi K2.5 is ~227% cheaper at $0.38/1M; pay for Gemini 2.5 Pro Preview 06-05 only for coding workflow support.

Specs

Released2025-03-252026-03-15
Context window1M256K
Parameters1T (MoE, 384 experts)
Architecturedecoder onlymixture of experts
LicenseUnknownMIT
Knowledge cutoff--

Pricing and availability

Gemini 2.5 Pro Preview 06-05Kimi K2.5
Input price$1.25/1M tokens$0.38/1M tokens
Output price$10/1M tokens$1.72/1M tokens
Providers

Capabilities

Gemini 2.5 Pro Preview 06-05Kimi K2.5
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 function calling: Kimi K2.5 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 Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $3.09 per million blended tokens. Availability is 1 providers versus 7, 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 Kimi K2.5 when coding workflow support, 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Gemini 2.5 Pro Preview 06-05 or Kimi K2.5?

Gemini 2.5 Pro Preview 06-05 supports 1M tokens, while Kimi K2.5 supports 256K 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 Kimi K2.5?

Kimi K2.5 is cheaper on tracked token pricing. Gemini 2.5 Pro Preview 06-05 costs $1.25/1M input and $10/1M output tokens. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemini 2.5 Pro Preview 06-05 or Kimi K2.5 open source?

Gemini 2.5 Pro Preview 06-05 is listed under Unknown. Kimi K2.5 is listed under MIT. 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 function calling, Gemini 2.5 Pro Preview 06-05 or Kimi K2.5?

Kimi K2.5 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for structured outputs, Gemini 2.5 Pro Preview 06-05 or Kimi K2.5?

Both Gemini 2.5 Pro Preview 06-05 and Kimi K2.5 expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Where can I run Gemini 2.5 Pro Preview 06-05 and Kimi K2.5?

Gemini 2.5 Pro Preview 06-05 is available on OpenRouter. Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.