DeepSeek-V4.1-Flash vs GPT-4.1 nano
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 1.6. GPT-4.1 nano is 1.9x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 1.6, ranking #12 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-4.1 nano is roughly 1.9x 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-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Sep 2026
- you need open weights you can self-host or fine-tune
Choose GPT-4.1 nano
- cost matters — it's about 1.9x 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
20 reported for DeepSeek-V4.1-Flash · 24 for GPT-4.1 nano
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while GPT-4.1 nano is better at 0 benchmarks.
DeepSeek-V4.1-Flash 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-V4.1-Flash ($0.22/1M tokens) is 2.2x more expensive than GPT-4.1 nano ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.6x more expensive than GPT-4.1 nano ($0.40/1M tokens).
In conclusion, DeepSeek-V4.1-Flash 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-V4.1-Flash's 1,040,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and GPT-4.1 nano support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
GPT-4.1 nano
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while GPT-4.1 nano was released on 2025-04-14.
DeepSeek-V4.1-Flash is 17 months newer than GPT-4.1 nano.
Sep 10, 2026
3 days ago
1.4yr 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-V4.1-Flash'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-V4.1-Flash's cutoff date.
—
May 2024
Provider Availability
DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. GPT-4.1 nano is available from OpenAI.
DeepSeek-V4.1-Flash
GPT-4.1 nano
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and GPT-4.1 nano side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs GPT-4.1 nano.