Kimi K2 vs Kimi K2 Instruct: which model should you choose?
We compared Kimi K2 and Kimi K2 Instruct on price, context window and 12 benchmarks. Kimi K2 has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
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
Kimi K2 Instruct
Kimi K2 Instruct is a multimodal model from Moonshot AI. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $23.99 per million output tokens, and averages 82.3 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $23.99 / 1M
- Context
- 1,000,000
- Avg score
- 82.3
- Open weights
- Yes
Specification comparison
| Spec | Kimi K2 | Kimi K2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.85 | $5.80 | B |
| Output price / 1M | $21.72 | $23.99 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 65,536 | 65,536 | Tie |
| Average score | 83.0 | 82.3 | A |
| Arena Elo | 1458.5 | 1458.6 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
Kimi K2 wins 5, Kimi K2 Instruct wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Kimi K2 | Kimi K2 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.3 | 86.5 | 3.2 | Kimi K2 Instruct |
| GPQA Diamond | 77.5 | 78.4 | 0.9 | Kimi K2 Instruct |
| SWE-bench Verified | 70.2 | 75.1 | 4.9 | Kimi K2 Instruct |
| HumanEval | 87.1 | 87.3 | 0.2 | Kimi K2 Instruct |
| MATH-500 | 85.3 | 88.0 | 2.7 | Kimi K2 Instruct |
| AIME 2025 | 85.1 | 77.3 | 7.8 | Kimi K2 |
| LiveCodeBench | 78.5 | 74.1 | 4.4 | Kimi K2 |
| IFEval | 91.5 | 87.5 | 4.0 | Kimi K2 |
| TruthfulQA | 84.8 | 82.8 | 2.0 | Kimi K2 |
| HellaSwag | 92.3 | 90.2 | 2.1 | Kimi K2 |
| MMMU | 77.5 | 78.3 | 0.8 | Kimi K2 Instruct |
| Arena Elo | 1458.5 | 1458.6 | 0.1 | Kimi K2 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, Kimi K2 costs about $9.82 per million tokens and Kimi K2 Instruct costs about $10.35. Kimi K2 is roughly 5% cheaper than Kimi K2 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Kimi K2 Instruct has the larger context window at 1,000,000 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, Kimi K2 wins 5, Kimi K2 Instruct wins 7, and 0 are tied. On the index average, Kimi K2 leads Kimi K2 Instruct by 0.7 points (83.0 versus 82.3).
The price picture favours Kimi K2. The context picture favours Kimi K2 Instruct. 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 Kimi K2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose Kimi K2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
Choose Kimi K2 Instruct if
- You need the larger 1,000,000-token context window.
FAQ
Is Kimi K2 better than Kimi K2 Instruct?
On the index average, Kimi K2 leads Kimi K2 Instruct by 0.7 points (83.0 versus 82.3). The better choice depends on price, context and your task. Kimi K2 wins 5 benchmarks, Kimi K2 Instruct wins 7.
Which is cheaper, Kimi K2 or Kimi K2 Instruct?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $10.35 for Kimi K2 Instruct.
Which has the larger context window?
Kimi K2 Instruct has the larger context window at 1,000,000 tokens. Kimi K2 has 128,000 tokens.
Are these models open weights?
Kimi K2 is open weights; Kimi K2 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.