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DeepSeek-V2.5 vs GPT-4.1 nano
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. GPT-4.1 nano offers 1.1M more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. GPT-4.1 nano supports multimodal inputs. DeepSeek-V2.5 is available on 3 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V2.5 was developed as an enhanced iteration of the DeepSeek-V2 architecture, designed to incorporate improvements in model quality and efficiency. Built to advance the DeepSeek foundation model series, it provides refined capabilities for general-purpose language understanding and generation tasks.
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OpenAI
GPT-4.1 Nano was developed as the smallest and most efficient variant in the GPT-4.1 family, designed for applications requiring minimal latency and resource usage. Built to enable AI capabilities on edge devices and resource-constrained environments, it distills GPT-4.1 capabilities into an ultra-compact form factor.
11 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
GPT-4.1 nano
OpenAI
2025-04-14
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V2.5
Input:$0.14
Output:$0.28($0.08 cheaper)
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GPT-4.1 nano
Input:$0.10
Output:$0.40
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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DeepSeek-V2.5
Average Score:80.4%(+0.3%)
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GPT-4.1 nano
Average Score:80.1%
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Knowledge Cutoff
Training data recency comparison
GPT-4.1 nano
2024-05-31
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-V2.5
3 providers
DeepInfra
Throughput: 63 tok/s
Latency: 0.5ms
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
Hyperbolic
Throughput: 100 tok/s
Latency: 0.5ms
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DeepSeek-V2.5
Avg Score:80.4%(+0.3%)
Providers:3
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GPT-4.1 nano
Avg Score:80.1%
Providers:1