DeepSeek R1 vs Kimi K2.5
DeepSeek R1 (2025) and Kimi K2.5 (2026) are agentic coding models from DeepSeek and Moonshot AI. DeepSeek R1 ships a 128K-token context window, while Kimi K2.5 ships a 256K-token context window. On Google-Proof Q&A, Kimi K2.5 leads by 16.4 pts. On pricing, DeepSeek R1 costs $0.1/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
DeepSeek R1 is ~283% cheaper at $0.1/1M; pay for Kimi K2.5 only for coding workflow support.
Specs
| Released | 2025-01-20 | 2026-03-15 |
| Context window | 128K | 256K |
| Parameters | 671B, 37B Active | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Open Source | MIT |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek R1 | Kimi K2.5 | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.38/1M tokens |
| Output price | $0.3/1M tokens | $1.72/1M tokens |
| Providers |
Capabilities
| DeepSeek R1 | Kimi K2.5 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek R1 | Kimi K2.5 |
|---|---|---|
| Google-Proof Q&A | 71.5 | 87.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek R1 at 71.5 and Kimi K2.5 at 87.9, with Kimi K2.5 ahead by 16.4 points. The largest visible gap is 16.4 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 reasoning mode: DeepSeek R1, function calling: Kimi K2.5, and code execution: DeepSeek R1. 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, DeepSeek R1 lists $0.1/1M input and $0.3/1M 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 DeepSeek R1 lower by about $0.62 per million blended tokens. Availability is 13 providers versus 7, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Kimi K2.5 when coding workflow support and larger context windows 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, DeepSeek R1 or Kimi K2.5?
Kimi K2.5 supports 256K tokens, while DeepSeek R1 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, DeepSeek R1 or Kimi K2.5?
DeepSeek R1 is cheaper on tracked token pricing. DeepSeek R1 costs $0.1/1M input and $0.3/1M 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 DeepSeek R1 or Kimi K2.5 open source?
DeepSeek R1 is listed under Open Source. 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 reasoning mode, DeepSeek R1 or Kimi K2.5?
DeepSeek R1 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, DeepSeek R1 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.
Where can I run DeepSeek R1 and Kimi K2.5?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. 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.