Gemini 2.5 Pro vs Kimi K2.5
Gemini 2.5 Pro (2025) and Kimi K2.5 (2026) are agentic coding models from Google DeepMind and Moonshot AI. Gemini 2.5 Pro ships a 1M-token context window, while Kimi K2.5 ships a 256K-token context window. On MMLU PRO, Kimi K2.5 leads by a hair. 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 only for coding workflow support.
Specs
| Released | 2025-06-17 | 2026-03-15 |
| Context window | 1M | 256K |
| Parameters | — | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | MIT |
| Knowledge cutoff | 2025-01 | - |
Pricing and availability
| Gemini 2.5 Pro | Kimi 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 | Kimi K2.5 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Gemini 2.5 Pro | Kimi K2.5 |
|---|---|---|
| MMLU PRO | 86.2 | 87.1 |
| Google-Proof Q&A | 86.4 | 87.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 2.5 Pro at 86.2 and Kimi K2.5 at 87.1, with Kimi K2.5 ahead by 0.9 points; Google-Proof Q&A has Gemini 2.5 Pro at 86.4 and Kimi K2.5 at 87.9, with Kimi K2.5 ahead by 1.5 points. The largest visible gap is 1.5 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, tool use: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. Both models share function calling and 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 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 3 providers versus 7, so concentration risk also matters.
Choose Gemini 2.5 Pro 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.
FAQ
Which has a larger context window, Gemini 2.5 Pro or Kimi K2.5?
Gemini 2.5 Pro 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 or Kimi K2.5?
Kimi K2.5 is cheaper on tracked token pricing. Gemini 2.5 Pro 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 or Kimi K2.5 open source?
Gemini 2.5 Pro is listed under Proprietary. 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 vision, Gemini 2.5 Pro or Kimi K2.5?
Gemini 2.5 Pro 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 or Kimi K2.5?
Gemini 2.5 Pro 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 and Kimi K2.5?
Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and 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.
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Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.