GPT-4.1 vs Kimi K2 Instruct: which model should you choose?
We compared GPT-4.1 and Kimi K2 Instruct on price, context window and 12 benchmarks. GPT-4.1 has the stronger average benchmark score; Kimi K2 Instruct is cheaper on a blended 3:1 basis.
GPT-4.1
GPT-4.1 is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $15.71 per million input tokens and $64.52 per million output tokens, and averages 82.4 across the index's benchmark suite.
- Input
- $15.71 / 1M
- Output
- $64.52 / 1M
- Context
- 1,000,000
- Avg score
- 82.4
- Open weights
- No
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 | GPT-4.1 | Kimi K2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $15.71 | $5.80 | B |
| Output price / 1M | $64.52 | $23.99 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 16,384 | 65,536 | B |
| Average score | 82.4 | 82.3 | A |
| Arena Elo | 1432.4 | 1458.6 | B |
| Open weights | No | Yes | B |
Benchmark comparison
GPT-4.1 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 | GPT-4.1 | Kimi K2 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.2 | 86.5 | 0.3 | Kimi K2 Instruct |
| GPQA Diamond | 76.1 | 78.4 | 2.3 | Kimi K2 Instruct |
| SWE-bench Verified | 72.2 | 75.1 | 2.9 | Kimi K2 Instruct |
| HumanEval | 88.3 | 87.3 | 1.0 | GPT-4.1 |
| MATH-500 | 86.8 | 88.0 | 1.2 | Kimi K2 Instruct |
| AIME 2025 | 79.9 | 77.3 | 2.6 | GPT-4.1 |
| LiveCodeBench | 77.8 | 74.1 | 3.7 | GPT-4.1 |
| IFEval | 90.4 | 87.5 | 2.9 | GPT-4.1 |
| TruthfulQA | 81.8 | 82.8 | 1.0 | Kimi K2 Instruct |
| HellaSwag | 90.3 | 90.2 | 0.1 | GPT-4.1 |
| MMMU | 76.3 | 78.3 | 2.0 | Kimi K2 Instruct |
| Arena Elo | 1432.4 | 1458.6 | 26.2 | Kimi K2 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, GPT-4.1 costs about $27.91 per million tokens and Kimi K2 Instruct costs about $10.35. Kimi K2 Instruct is roughly 63% cheaper than GPT-4.1. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-4.1 wins 5, Kimi K2 Instruct wins 7, and 0 are tied. On the index average, GPT-4.1 leads Kimi K2 Instruct by 0.1 points (82.4 versus 82.3).
The price picture favours Kimi K2 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 GPT-4.1 for quality-sensitive work and Kimi K2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GPT-4.1 if
- You want the higher average benchmark score in this index.
Choose Kimi K2 Instruct 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 GPT-4.1 better than Kimi K2 Instruct?
On the index average, GPT-4.1 leads Kimi K2 Instruct by 0.1 points (82.4 versus 82.3). The better choice depends on price, context and your task. GPT-4.1 wins 5 benchmarks, Kimi K2 Instruct wins 7.
Which is cheaper, GPT-4.1 or Kimi K2 Instruct?
Kimi K2 Instruct is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $27.91 for GPT-4.1.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
GPT-4.1 is proprietary; Kimi K2 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with GPT-4.1 for quality-sensitive work and Kimi K2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.