Model Comparison
DeepSeek-V3.2 (Thinking) vs GPT-5 nanoWhich is better in 2026?
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. GPT-5 nano is 2.3x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs GPT-5 nano — which is better?
DeepSeek-V3.2 (Thinking) (by DeepSeek) and GPT-5 nano (by OpenAI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, HMMT 2025, Humanity's Last Exam), while GPT-5 nano is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
On price, GPT-5 nano is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5 nano also accepts a larger context window (400,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2 (Thinking) if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- you want the most recent training data — it shipped Dec 2025
- you need open weights you can self-host or fine-tune
Choose GPT-5 nano if…
- cost matters — it's about 2.3x cheaper per token
- you process long inputs — it offers a 400,000 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, HMMT 2025, Humanity's Last Exam), while GPT-5 nano is better at 0 benchmarks.
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 5.6x more expensive than GPT-5 nano ($0.05/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 1.0x more expensive than GPT-5 nano ($0.40/1M tokens).
In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than GPT-5 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5 nano accepts 400,000 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. GPT-5 nano can generate longer responses up to 128,000 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
GPT-5 nano supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.
GPT-5 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Thinking)
GPT-5 nano
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) 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.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while GPT-5 nano was released on 2025-08-07.
DeepSeek-V3.2 (Thinking) is 4 months newer than GPT-5 nano.
Dec 1, 2025
6 months ago
3mo newerAug 7, 2025
10 months ago
Knowledge Cutoff
When training data ends
GPT-5 nano has a documented knowledge cutoff of 2024-05-30, while DeepSeek-V3.2 (Thinking)'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-V3.2 (Thinking)'s cutoff date.
—
May 2024
Provider Availability
DeepSeek-V3.2 (Thinking) is available from DeepSeek. GPT-5 nano is available from OpenAI.
DeepSeek-V3.2 (Thinking)
GPT-5 nano
Outputs Comparison
Key Takeaways
GPT-5 nano
View detailsOpenAI
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs GPT-5 nano.