Model Comparison

DeepSeek-V3.1 vs GPT-5 nano

Both models are evenly matched across the benchmarks. GPT-5 nano is 3.3x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3.1 outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while GPT-5 nano is better at 2 benchmarks (AIME 2025, HMMT 2025).

Both models are evenly matched across the benchmarks.

Tue May 26 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GPT-5 nano costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 5.4x more expensive than GPT-5 nano ($0.05/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 2.5x more expensive than GPT-5 nano ($0.40/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than GPT-5 nano.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue May 26 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
OpenAI
GPT-5 nano
Input tokens$0.05
Output tokens$0.40
Best providerOpenAI
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

GPT-5 nano accepts 400,000 input tokens compared to DeepSeek-V3.1's 163,840 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while GPT-5 nano is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
OpenAI
GPT-5 nano
Input400,000 tokens
Output128,000 tokens
Tue May 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GPT-5 nano supports multimodal inputs, whereas DeepSeek-V3.1 does not.

GPT-5 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

GPT-5 nano

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while GPT-5 nano uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.1

MIT

Open weights

GPT-5 nano

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while GPT-5 nano was released on 2025-08-07.

GPT-5 nano is 7 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.4 years ago

GPT-5 nano

Aug 7, 2025

9 months ago

6mo newer

Knowledge Cutoff

When training data ends

GPT-5 nano has a documented knowledge cutoff of 2024-05-30, while DeepSeek-V3.1's cutoff date is not specified.

We can confirm GPT-5 nano's training data extends to 2024-05-30, but cannot make a direct comparison without DeepSeek-V3.1's cutoff date.

DeepSeek-V3.1

—

GPT-5 nano

May 2024

Provider Availability

DeepSeek-V3.1 is available from DeepInfra, Novita. GPT-5 nano is available from OpenAI.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

GPT-5 nano

openai logo
OpenAI
Input Price:Input: $0.05/1MOutput Price:Output: $0.40/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Has open weights
Higher GPQA score (74.9% vs 71.2%)
Higher Humanity's Last Exam score (15.9% vs 8.7%)
OpenAI
Larger context window (400,000 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens
Higher AIME 2025 score (85.2% vs 49.8%)
Higher HMMT 2025 score (75.6% vs 33.5%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
OpenAI
GPT-5 nano

FAQ

Common questions about DeepSeek-V3.1 vs GPT-5 nano.

Which is better, DeepSeek-V3.1 or GPT-5 nano?

Both models are evenly matched across the benchmarks. DeepSeek-V3.1 is made by DeepSeek and GPT-5 nano is made by OpenAI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.1 compare to GPT-5 nano in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. GPT-5 nano scores AIME 2025: 85.2%, HMMT 2025: 75.6%, GPQA: 71.2%, FrontierMath: 9.6%, Humanity's Last Exam: 8.7%.

Is DeepSeek-V3.1 cheaper than GPT-5 nano?

GPT-5 nano is 5.4x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. GPT-5 nano costs $0.05/M input and $0.40/M output via openai.

What are the context window sizes for DeepSeek-V3.1 and GPT-5 nano?

DeepSeek-V3.1 supports 164K tokens and GPT-5 nano supports 400K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.1 and GPT-5 nano?

Key differences include context window (164K vs 400K), input pricing ($0.27 vs $0.05/M), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and GPT-5 nano?

DeepSeek-V3.1 is developed by DeepSeek and GPT-5 nano is developed by OpenAI.