Claude Haiku 4.5 vs Kimi K3
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Claude Haiku 4.5 | Kimi K3 |
|---|---|---|
| Provider | Anthropic | Moonshot AI |
| Input Price (1M) | $1.00 | $3.00 |
| Output Price (1M) | $5.00 | $15.00 |
| Context Window | 200,000 | 1,000,000 |
Verdict
Claude Haiku 4.5 costs $1.00 per 1M input tokens and $5.00 per 1M output tokens. Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. Claude Haiku 4.5 is 67% cheaper on input tokens than Kimi K3. For output tokens, Claude Haiku 4.5 is the more affordable option at $5.00/1M vs $15.00.
On context window, Kimi K3 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Claude Haiku 4.5
- ✓ You need the lowest input token cost ($ 1.00/1M)
- ✓ Your workload is output-heavy — Claude Haiku 4.5 generates text cheaper
- ✓ You are already integrated with Anthropic
When to choose Kimi K3
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Moonshot AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Claude Haiku 4.5 cheaper than Kimi K3? ▼
Claude Haiku 4.5 is cheaper on input tokens at $1.00/1M vs $3.00/1M for Kimi K3 — a 67% saving.
What is the context window of Claude Haiku 4.5 vs Kimi K3? ▼
Claude Haiku 4.5 has a 200,000-token context window. Kimi K3 has a 1,000,000-token context window. Kimi K3 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Claude Haiku 4.5 or Kimi K3? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Claude Haiku 4.5 is the cheaper option. For maximum context length, Kimi K3 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.