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DeepSeek-V3 vs GPT-5 nano
Comprehensive side-by-side LLM comparison
GPT-5 nano leads with 12.1% higher average benchmark score. GPT-5 nano offers 265.9K more tokens in context window than DeepSeek-V3. GPT-5 nano is $0.92 cheaper per million tokens. GPT-5 nano supports multimodal inputs. Overall, GPT-5 nano is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
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OpenAI
GPT-5 Nano was developed as the most compact variant in the GPT-5 family, designed for deployment in resource-constrained environments and edge computing scenarios. Built to bring next-generation AI capabilities to devices and applications where latency and efficiency are paramount, it distills GPT-5 innovations into a minimal footprint.
7 months newer
DeepSeek-V3
DeepSeek
2024-12-25
GPT-5 nano
OpenAI
2025-08-07
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V3
Input:$0.27
Output:$1.10
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GPT-5 nano
Input:$0.05
Output:$0.40($0.92 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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DeepSeek-V3
Average Score:59.1%
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GPT-5 nano
Average Score:71.2%(+12.1%)
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Knowledge Cutoff
Training data recency comparison
GPT-5 nano
2024-05-30
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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GPT-5 nano
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DeepSeek-V3
Avg Score:59.1%
Providers:1
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GPT-5 nano
Avg Score:71.2%(+12.1%)
Providers:2