Claude Haiku 4.5 vs Kimi K2 Thinking
Claude Haiku 4.5 (2025) and Kimi K2 Thinking (2025) are frontier reasoning models from Anthropic and Moonshot AI. Claude Haiku 4.5 ships a 200k-token context window, while Kimi K2 Thinking ships a 256K-token context window. On pricing, Kimi K2 Thinking costs $0.6/1M input tokens versus $0.8/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Claude Haiku 4.5 is safer overall; choose Kimi K2 Thinking when reasoning depth matters.
Specs
| Released | 2025-10-01 | 2025-01-01 |
| Context window | 200k | 256K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-02 | - |
Pricing and availability
| Claude Haiku 4.5 | Kimi K2 Thinking | |
|---|---|---|
| Input price | $0.8/1M tokens | $0.6/1M tokens |
| Output price | $4/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Claude Haiku 4.5 | 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 vision: Claude Haiku 4.5, multimodal input: Claude Haiku 4.5, reasoning mode: Kimi K2 Thinking, function calling: Claude Haiku 4.5, tool use: Claude Haiku 4.5, and code execution: Claude Haiku 4.5. 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, Claude Haiku 4.5 lists $0.8/1M input and $4/1M 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 Thinking lower by about $0.59 per million blended tokens. Availability is 8 providers versus 5, so concentration risk also matters.
Choose Claude Haiku 4.5 when coding workflow support and broader provider choice are central to the workload. Choose Kimi K2 Thinking when reasoning depth, 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 Haiku 4.5 or Kimi K2 Thinking?
Kimi K2 Thinking supports 256K tokens, while Claude Haiku 4.5 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 Haiku 4.5 or Kimi K2 Thinking?
Kimi K2 Thinking is cheaper on tracked token pricing. Claude Haiku 4.5 costs $0.8/1M input and $4/1M 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 Claude Haiku 4.5 or Kimi K2 Thinking open source?
Claude Haiku 4.5 is listed under Proprietary. 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 vision, Claude Haiku 4.5 or Kimi K2 Thinking?
Claude Haiku 4.5 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 Haiku 4.5 or Kimi K2 Thinking?
Claude Haiku 4.5 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 Haiku 4.5 and Kimi K2 Thinking?
Claude Haiku 4.5 is available on Microsoft Foundry, Anthropic, Snowflake Cortex, AWS Bedrock, and GCP Vertex AI. 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.