Claude Haiku 4.5 Max vs Kimi K2: which model should you choose?
We compared Claude Haiku 4.5 Max and Kimi K2 on price, context window and 12 benchmarks. Claude Haiku 4.5 Max has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
Claude Haiku 4.5 Max
Claude Haiku 4.5 Max is a multimodal model from Anthropic. It offers a 200,000-token context window, costs $15.98 per million input tokens and $80.37 per million output tokens, and averages 83.8 across the index's benchmark suite.
- Input
- $15.98 / 1M
- Output
- $80.37 / 1M
- Context
- 200,000
- Avg score
- 83.8
- Open weights
- No
Kimi K2
Kimi K2 is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.85 per million input tokens and $21.72 per million output tokens, and averages 83.0 across the index's benchmark suite.
- Input
- $5.85 / 1M
- Output
- $21.72 / 1M
- Context
- 128,000
- Avg score
- 83.0
- Open weights
- Yes
Specification comparison
| Spec | Claude Haiku 4.5 Max | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $15.98 | $5.85 | B |
| Output price / 1M | $80.37 | $21.72 | B |
| Context window | 200,000 | 128,000 | A |
| Max output | 4,096 | 65,536 | B |
| Average score | 83.8 | 83.0 | A |
| Arena Elo | 1452.0 | 1458.5 | B |
| Open weights | No | Yes | B |
Benchmark comparison
Claude Haiku 4.5 Max wins 7, Kimi K2 wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Claude Haiku 4.5 Max | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.6 | 83.3 | 3.3 | Claude Haiku 4.5 Max |
| GPQA Diamond | 80.3 | 77.5 | 2.8 | Claude Haiku 4.5 Max |
| SWE-bench Verified | 74.0 | 70.2 | 3.8 | Claude Haiku 4.5 Max |
| HumanEval | 88.5 | 87.1 | 1.4 | Claude Haiku 4.5 Max |
| MATH-500 | 88.7 | 85.3 | 3.4 | Claude Haiku 4.5 Max |
| AIME 2025 | 78.2 | 85.1 | 6.9 | Kimi K2 |
| LiveCodeBench | 79.2 | 78.5 | 0.7 | Claude Haiku 4.5 Max |
| IFEval | 89.9 | 91.5 | 1.6 | Kimi K2 |
| TruthfulQA | 83.7 | 84.8 | 1.1 | Kimi K2 |
| HellaSwag | 91.1 | 92.3 | 1.2 | Kimi K2 |
| MMMU | 81.4 | 77.5 | 3.9 | Claude Haiku 4.5 Max |
| Arena Elo | 1452.0 | 1458.5 | 6.5 | Kimi K2 |
Price analysis
On a blended 3:1 input-to-output basis, Claude Haiku 4.5 Max costs about $32.08 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 69% cheaper than Claude Haiku 4.5 Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Claude Haiku 4.5 Max has the larger context window at 200,000 tokens, compared with 128,000 for Kimi K2. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Claude Haiku 4.5 Max wins 7, Kimi K2 wins 5, and 0 are tied. On the index average, Claude Haiku 4.5 Max leads Kimi K2 by 0.8 points (83.8 versus 83.0).
The price picture favours Kimi K2. The context picture favours Claude Haiku 4.5 Max. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Claude Haiku 4.5 Max for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Claude Haiku 4.5 Max if
- You want the higher average benchmark score in this index.
- You need the larger 200,000-token context window.
Choose Kimi K2 if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is Claude Haiku 4.5 Max better than Kimi K2?
On the index average, Claude Haiku 4.5 Max leads Kimi K2 by 0.8 points (83.8 versus 83.0). The better choice depends on price, context and your task. Claude Haiku 4.5 Max wins 7 benchmarks, Kimi K2 wins 5.
Which is cheaper, Claude Haiku 4.5 Max or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $32.08 for Claude Haiku 4.5 Max.
Which has the larger context window?
Claude Haiku 4.5 Max has the larger context window at 200,000 tokens. Kimi K2 has 128,000 tokens.
Are these models open weights?
Claude Haiku 4.5 Max is proprietary; Kimi K2 is open weights.
Which should I choose for production?
If you need a single default, start with Claude Haiku 4.5 Max for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.