DeepSeek-R1 vs GPT-4.1: which model should you choose?
We compared DeepSeek-R1 and GPT-4.1 on price, context window and 12 benchmarks. DeepSeek-R1 has the stronger average benchmark score; DeepSeek-R1 is cheaper on a blended 3:1 basis.
DeepSeek-R1
DeepSeek-R1 is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.83 per million input tokens and $25.23 per million output tokens, and averages 82.8 across the index's benchmark suite.
- Input
- $5.83 / 1M
- Output
- $25.23 / 1M
- Context
- 128,000
- Avg score
- 82.8
- Open weights
- Yes
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
Specification comparison
| Spec | DeepSeek-R1 | GPT-4.1 | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $15.71 | A |
| Output price / 1M | $25.23 | $64.52 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 4,096 | 16,384 | B |
| Average score | 82.8 | 82.4 | A |
| Arena Elo | 1454.1 | 1432.4 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-R1 wins 7, GPT-4.1 wins 4, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | GPT-4.1 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 86.2 | 2.4 | GPT-4.1 |
| GPQA Diamond | 77.8 | 76.1 | 1.7 | DeepSeek-R1 |
| SWE-bench Verified | 72.2 | 72.2 | 0.0 | Tie |
| HumanEval | 86.4 | 88.3 | 1.9 | GPT-4.1 |
| MATH-500 | 89.1 | 86.8 | 2.3 | DeepSeek-R1 |
| AIME 2025 | 81.1 | 79.9 | 1.2 | DeepSeek-R1 |
| LiveCodeBench | 77.4 | 77.8 | 0.4 | GPT-4.1 |
| IFEval | 91.0 | 90.4 | 0.6 | DeepSeek-R1 |
| TruthfulQA | 82.9 | 81.8 | 1.1 | DeepSeek-R1 |
| HellaSwag | 89.8 | 90.3 | 0.5 | GPT-4.1 |
| MMMU | 79.5 | 76.3 | 3.2 | DeepSeek-R1 |
| Arena Elo | 1454.1 | 1432.4 | 21.7 | DeepSeek-R1 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 costs about $10.68 per million tokens and GPT-4.1 costs about $27.91. DeepSeek-R1 is roughly 62% cheaper than GPT-4.1. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-4.1 has the larger context window at 1,000,000 tokens, compared with 128,000 for DeepSeek-R1. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 wins 7, GPT-4.1 wins 4, and 1 are tied. On the index average, DeepSeek-R1 leads GPT-4.1 by 0.4 points (82.8 versus 82.4).
The price picture favours DeepSeek-R1. The context picture favours GPT-4.1. 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 DeepSeek-R1 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 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.
Choose GPT-4.1 if
- You need the larger 1,000,000-token context window.
- You need the larger 16,384-token output limit.
FAQ
Is DeepSeek-R1 better than GPT-4.1?
On the index average, DeepSeek-R1 leads GPT-4.1 by 0.4 points (82.8 versus 82.4). The better choice depends on price, context and your task. DeepSeek-R1 wins 7 benchmarks, GPT-4.1 wins 4.
Which is cheaper, DeepSeek-R1 or GPT-4.1?
DeepSeek-R1 is cheaper on a blended 3:1 basis at about $10.68 per million tokens, versus $27.91 for GPT-4.1.
Which has the larger context window?
GPT-4.1 has the larger context window at 1,000,000 tokens. DeepSeek-R1 has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 is open weights; GPT-4.1 is proprietary.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 for quality-sensitive work, and check whether its price holds at your expected volume before committing.