DeepSeek-R1-0528 vs GPT-4.1 nano
DeepSeek-R1-0528 leads the LLM Stats Score 24.2 to 1.7. GPT-4.1 nano is 5.2x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-R1-0528 leads the overall LLM Stats Score 24.2 to 1.7, ranking #165 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-R1-0528 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-4.1 nano is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-4.1 nano also accepts a larger context window (1,047,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- overall performance matters — it scores 24.2 and ranks #165 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you want the most recent training data — it shipped May 2025
- you need open weights you can self-host or fine-tune
Choose GPT-4.1 nano
- cost matters — it's about 5.2x cheaper per token
- you process long inputs — it offers a 1,047,576 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 24 for GPT-4.1 nano
DeepSeek-R1-0528 outperforms in 3 benchmarks (Aider-Polyglot, AIME 2024, GPQA), while GPT-4.1 nano is better at 0 benchmarks.
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 5.0x more expensive than GPT-4.1 nano ($0.10/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 5.4x more expensive than GPT-4.1 nano ($0.40/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than GPT-4.1 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-4.1 nano accepts 1,047,576 input tokens compared to DeepSeek-R1-0528's 163,840 tokens. DeepSeek-R1-0528 can generate longer responses up to 163,840 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4.1 nano supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
GPT-4.1 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
GPT-4.1 nano
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while GPT-4.1 nano uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while GPT-4.1 nano was released on 2025-04-14.
DeepSeek-R1-0528 is 1 month newer than GPT-4.1 nano.
May 28, 2025
1.3 years ago
1mo newerApr 14, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while DeepSeek-R1-0528's cutoff date is not specified.
We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without DeepSeek-R1-0528's cutoff date.
—
May 2024
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. GPT-4.1 nano is available from OpenAI.
DeepSeek-R1-0528
GPT-4.1 nano
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and GPT-4.1 nano side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs GPT-4.1 nano.