Command A Pro vs Kimi K1.5 Instruct: which model should you choose?
We compared Command A Pro and Kimi K1.5 Instruct on price, context window and 12 benchmarks. Command A Pro has the stronger average benchmark score; Kimi K1.5 Instruct is cheaper on a blended 3:1 basis.
Command A Pro
Command A Pro is a multimodal model from Cohere. It offers a 200,000-token context window, costs $14.28 per million input tokens and $68.05 per million output tokens, and averages 81.3 across the index's benchmark suite.
- Input
- $14.28 / 1M
- Output
- $68.05 / 1M
- Context
- 200,000
- Avg score
- 81.3
- Open weights
- No
Kimi K1.5 Instruct
Kimi K1.5 Instruct is a multimodal model from Moonshot AI. It offers a 200,000-token context window, costs $5.15 per million input tokens and $28.20 per million output tokens, and averages 80.5 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $28.20 / 1M
- Context
- 200,000
- Avg score
- 80.5
- Open weights
- Yes
Specification comparison
| Spec | Command A Pro | Kimi K1.5 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.28 | $5.15 | B |
| Output price / 1M | $68.05 | $28.20 | B |
| Context window | 200,000 | 200,000 | Tie |
| Max output | 16,384 | 4,096 | A |
| Average score | 81.3 | 80.5 | A |
| Arena Elo | 1434.6 | 1431.8 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Command A Pro wins 7, Kimi K1.5 Instruct wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Command A Pro | Kimi K1.5 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.3 | 83.0 | 1.7 | Kimi K1.5 Instruct |
| GPQA Diamond | 79.1 | 77.4 | 1.7 | Command A Pro |
| SWE-bench Verified | 71.2 | 67.8 | 3.4 | Command A Pro |
| HumanEval | 87.5 | 84.0 | 3.5 | Command A Pro |
| MATH-500 | 83.1 | 82.6 | 0.5 | Command A Pro |
| AIME 2025 | 78.2 | 80.4 | 2.2 | Kimi K1.5 Instruct |
| LiveCodeBench | 74.0 | 71.1 | 2.9 | Command A Pro |
| IFEval | 89.0 | 86.2 | 2.8 | Command A Pro |
| TruthfulQA | 83.3 | 84.3 | 1.0 | Kimi K1.5 Instruct |
| HellaSwag | 90.1 | 90.2 | 0.1 | Kimi K1.5 Instruct |
| MMMU | 77.2 | 78.6 | 1.4 | Kimi K1.5 Instruct |
| Arena Elo | 1434.6 | 1431.8 | 2.8 | Command A Pro |
Price analysis
On a blended 3:1 input-to-output basis, Command A Pro costs about $27.72 per million tokens and Kimi K1.5 Instruct costs about $10.91. Kimi K1.5 Instruct is roughly 61% cheaper than Command A Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 200,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Command A Pro wins 7, Kimi K1.5 Instruct wins 5, and 0 are tied. On the index average, Command A Pro leads Kimi K1.5 Instruct by 0.8 points (81.3 versus 80.5).
The price picture favours Kimi K1.5 Instruct. The context picture is effectively a tie. 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 Command A Pro for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Command A Pro if
- You want the higher average benchmark score in this index.
- You need the larger 16,384-token output limit.
Choose Kimi K1.5 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is Command A Pro better than Kimi K1.5 Instruct?
On the index average, Command A Pro leads Kimi K1.5 Instruct by 0.8 points (81.3 versus 80.5). The better choice depends on price, context and your task. Command A Pro wins 7 benchmarks, Kimi K1.5 Instruct wins 5.
Which is cheaper, Command A Pro or Kimi K1.5 Instruct?
Kimi K1.5 Instruct is cheaper on a blended 3:1 basis at about $10.91 per million tokens, versus $27.72 for Command A Pro.
Which has the larger context window?
Both models list a 200,000-token context window.
Are these models open weights?
Command A Pro is proprietary; Kimi K1.5 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Command A Pro for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.