Claude 3.7 Sonnet vs Kimi K2.5
Claude 3.7 Sonnet (2024) and Kimi K2.5 (2026) are agentic coding models from Anthropic and Moonshot AI. Claude 3.7 Sonnet ships a 200K-token context window, while Kimi K2.5 ships a 256K-token context window. On MMLU PRO, Kimi K2.5 leads by 6.8 pts. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 is ~684% cheaper at $0.38/1M; pay for Claude 3.7 Sonnet only for coding workflow support.
Specs
| Released | 2024-03-04 | 2026-03-15 |
| Context window | 200K | 256K |
| Parameters | — | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | MIT |
| Knowledge cutoff | 2024-11 | - |
Pricing and availability
| Claude 3.7 Sonnet | Kimi K2.5 | |
|---|---|---|
| Input price | $3/1M tokens | $0.38/1M tokens |
| Output price | $15/1M tokens | $1.72/1M tokens |
| Providers |
Capabilities
| Claude 3.7 Sonnet | Kimi K2.5 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Claude 3.7 Sonnet | Kimi K2.5 |
|---|---|---|
| MMLU PRO | 80.3 | 87.1 |
| Massive Multi-discipline Multimodal Understanding | 75.0 | 84.3 |
Deep dive
On shared benchmark coverage, MMLU PRO has Claude 3.7 Sonnet at 80.3 and Kimi K2.5 at 87.1, with Kimi K2.5 ahead by 6.8 points; Massive Multi-discipline Multimodal Understanding has Claude 3.7 Sonnet at 75 and Kimi K2.5 at 84.3, with Kimi K2.5 ahead by 9.3 points. The largest visible gap is 9.3 points on Massive Multi-discipline Multimodal Understanding, 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: Claude 3.7 Sonnet, multimodal input: Claude 3.7 Sonnet, reasoning mode: Claude 3.7 Sonnet, tool use: Claude 3.7 Sonnet, and code execution: Claude 3.7 Sonnet. 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, Claude 3.7 Sonnet lists $3/1M input and $15/1M 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 $5.82 per million blended tokens. Availability is 6 providers versus 7, so concentration risk also matters.
Choose Claude 3.7 Sonnet when coding workflow support are central to the workload. Choose Kimi K2.5 when coding workflow support, larger context windows, and lower input-token cost 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, Claude 3.7 Sonnet or Kimi K2.5?
Kimi K2.5 supports 256K tokens, while Claude 3.7 Sonnet supports 200K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Claude 3.7 Sonnet or Kimi K2.5?
Kimi K2.5 is cheaper on tracked token pricing. Claude 3.7 Sonnet costs $3/1M input and $15/1M 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 Claude 3.7 Sonnet or Kimi K2.5 open source?
Claude 3.7 Sonnet 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, Claude 3.7 Sonnet or Kimi K2.5?
Claude 3.7 Sonnet 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, Claude 3.7 Sonnet or Kimi K2.5?
Claude 3.7 Sonnet 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 Claude 3.7 Sonnet and Kimi K2.5?
Claude 3.7 Sonnet is available on Snowflake Cortex, GCP Vertex AI, Replicate API, OpenRouter, and AWS Bedrock. 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.