Kimi K2.5 vs Kimi K2 Thinking
Kimi K2.5 (2026) and Kimi K2 Thinking (2025) are agentic coding models from Moonshot AI. Kimi K2.5 ships a 256K-token context window, while Kimi K2 Thinking ships a 256K-token context window. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $0.6/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Kimi K2.5 is ~57% cheaper at $0.38/1M; pay for Kimi K2 Thinking only for reasoning depth.
Specs
| Released | 2026-03-15 | 2025-01-01 |
| Context window | 256K | 256K |
| Parameters | 1T (MoE, 384 experts) | — |
| Architecture | mixture of experts | decoder only |
| License | MIT | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Kimi K2.5 | Kimi K2 Thinking | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.6/1M tokens |
| Output price | $1.72/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Kimi K2 Thinking | |
|---|---|---|
| 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 reasoning mode: Kimi K2 Thinking and function calling: Kimi K2.5. 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, Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens, while Kimi K2 Thinking lists $0.6/1M input and $2.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.39 per million blended tokens. Availability is 7 providers versus 5, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Kimi K2 Thinking when reasoning depth 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, Kimi K2.5 or Kimi K2 Thinking?
Kimi K2.5 supports 256K tokens, while Kimi K2 Thinking 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, Kimi K2.5 or Kimi K2 Thinking?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Kimi K2 Thinking costs $0.6/1M input and $2.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Kimi K2 Thinking open source?
Kimi K2.5 is listed under MIT. Kimi K2 Thinking is listed under Proprietary. 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 reasoning mode, Kimi K2.5 or Kimi K2 Thinking?
Kimi K2 Thinking has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Kimi K2.5 or Kimi K2 Thinking?
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.
Where can I run Kimi K2.5 and Kimi K2 Thinking?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Kimi K2 Thinking is available on Fireworks AI, GCP Vertex AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. 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.