Head-to-head · multimodal vs frontier

DeepSeek-R1 vs GPT-4.1 Turbo: which model should you choose?

We compared DeepSeek-R1 and GPT-4.1 Turbo on price, context window and 12 benchmarks. GPT-4.1 Turbo has the stronger average benchmark score; DeepSeek-R1 is cheaper on a blended 3:1 basis.

DeepSeek

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
Full DeepSeek-R1 page
OpenAI

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
Full GPT-4.1 Turbo page

Specification comparison

SpecDeepSeek-R1GPT-4.1 TurboEdge
Input price / 1M$5.83$16.12A
Output price / 1M$25.23$69.91A
Context window128,0001,000,000B
Max output4,096128,000B
Average score82.882.9B
Arena Elo1454.11446.2A
Open weightsYesNoA

Benchmark comparison

DeepSeek-R1 wins 6, GPT-4.1 Turbo wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkDeepSeek-R1GPT-4.1 TurboGapWinner
MMLU-Pro83.884.60.8GPT-4.1 Turbo
GPQA Diamond77.876.11.7DeepSeek-R1
SWE-bench Verified72.274.72.5GPT-4.1 Turbo
HumanEval86.489.83.4GPT-4.1 Turbo
MATH-50089.186.92.2DeepSeek-R1
AIME 202581.177.43.7DeepSeek-R1
LiveCodeBench77.478.71.3GPT-4.1 Turbo
IFEval91.088.22.8DeepSeek-R1
TruthfulQA82.987.04.1GPT-4.1 Turbo
HellaSwag89.890.00.2GPT-4.1 Turbo
MMMU79.578.51.0DeepSeek-R1
Arena Elo1454.11446.27.9DeepSeek-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 Turbo costs about $29.57. DeepSeek-R1 is roughly 64% 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 DeepSeek-R1. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, DeepSeek-R1 wins 6, GPT-4.1 Turbo wins 6, and 0 are tied. On the index average, GPT-4.1 Turbo leads DeepSeek-R1 by 0.1 points (82.9 versus 82.8).

The price picture favours DeepSeek-R1. 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 GPT-4.1 Turbo for quality-sensitive work and DeepSeek-R1 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose DeepSeek-R1 if

  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

Choose GPT-4.1 Turbo if

  • You want the higher average benchmark score in this index.
  • You need the larger 1,000,000-token context window.
  • You need the larger 128,000-token output limit.

FAQ

Is DeepSeek-R1 better than GPT-4.1 Turbo?

On the index average, GPT-4.1 Turbo leads DeepSeek-R1 by 0.1 points (82.9 versus 82.8). The better choice depends on price, context and your task. DeepSeek-R1 wins 6 benchmarks, GPT-4.1 Turbo wins 6.

Which is cheaper, DeepSeek-R1 or GPT-4.1 Turbo?

DeepSeek-R1 is cheaper on a blended 3:1 basis at about $10.68 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. DeepSeek-R1 has 128,000 tokens.

Are these models open weights?

DeepSeek-R1 is open weights; GPT-4.1 Turbo is proprietary.

Which should I choose for production?

If you need a single default, start with GPT-4.1 Turbo for quality-sensitive work and DeepSeek-R1 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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