GPT-4.1 Turbo vs Kimi K2: which model should you choose?
We compared GPT-4.1 Turbo and Kimi K2 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.
GPT-4.1 Turbo
GPT-4.1 Turbo is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $16.12 per million input tokens and $69.91 per million output tokens, and averages 82.9 across the index's benchmark suite.
- Input
- $16.12 / 1M
- Output
- $69.91 / 1M
- Context
- 1,000,000
- Avg score
- 82.9
- 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 | GPT-4.1 Turbo | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $16.12 | $5.85 | B |
| Output price / 1M | $69.91 | $21.72 | B |
| Context window | 1,000,000 | 128,000 | A |
| Max output | 128,000 | 65,536 | A |
| Average score | 82.9 | 83.0 | B |
| Arena Elo | 1446.2 | 1458.5 | B |
| Open weights | No | Yes | B |
Benchmark comparison
GPT-4.1 Turbo wins 7, Kimi K2 wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-4.1 Turbo | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.6 | 83.3 | 1.3 | GPT-4.1 Turbo |
| GPQA Diamond | 76.1 | 77.5 | 1.4 | Kimi K2 |
| SWE-bench Verified | 74.7 | 70.2 | 4.5 | GPT-4.1 Turbo |
| HumanEval | 89.8 | 87.1 | 2.7 | GPT-4.1 Turbo |
| MATH-500 | 86.9 | 85.3 | 1.6 | GPT-4.1 Turbo |
| AIME 2025 | 77.4 | 85.1 | 7.7 | Kimi K2 |
| LiveCodeBench | 78.7 | 78.5 | 0.2 | GPT-4.1 Turbo |
| IFEval | 88.2 | 91.5 | 3.3 | Kimi K2 |
| TruthfulQA | 87.0 | 84.8 | 2.2 | GPT-4.1 Turbo |
| HellaSwag | 90.0 | 92.3 | 2.3 | Kimi K2 |
| MMMU | 78.5 | 77.5 | 1.0 | GPT-4.1 Turbo |
| Arena Elo | 1446.2 | 1458.5 | 12.3 | Kimi K2 |
Price analysis
On a blended 3:1 input-to-output basis, GPT-4.1 Turbo costs about $29.57 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 67% cheaper than GPT-4.1 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-4.1 Turbo 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, GPT-4.1 Turbo wins 7, Kimi K2 wins 5, and 0 are tied. On the index average, Kimi K2 leads GPT-4.1 Turbo by 0.1 points (83.0 versus 82.9).
The price picture favours Kimi K2. The context picture favours GPT-4.1 Turbo. 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 GPT-4.1 Turbo if
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
Choose Kimi K2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is GPT-4.1 Turbo better than Kimi K2?
On the index average, Kimi K2 leads GPT-4.1 Turbo by 0.1 points (83.0 versus 82.9). The better choice depends on price, context and your task. GPT-4.1 Turbo wins 7 benchmarks, Kimi K2 wins 5.
Which is cheaper, GPT-4.1 Turbo or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $29.57 for GPT-4.1 Turbo.
Which has the larger context window?
GPT-4.1 Turbo has the larger context window at 1,000,000 tokens. Kimi K2 has 128,000 tokens.
Are these models open weights?
GPT-4.1 Turbo is proprietary; Kimi K2 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.