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GPT-4.1 nano vs Mistral Small 3.1 24B Base
Comprehensive side-by-side LLM comparison
GPT-4.1 nano leads with 2.7% higher average benchmark score. GPT-4.1 nano offers 824.3K more tokens in context window than Mistral Small 3.1 24B Base. Both models have similar pricing. Both models have their strengths depending on your specific coding needs.
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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.
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Mistral AI
Mistral Small 3.1 24B Base represents an updated iteration of the 24B foundation model, developed with architectural refinements and improved training. Built to provide enhanced base capabilities for fine-tuning, it incorporates learnings from previous versions for better downstream performance.
28 days newer
Mistral Small 3.1 24B Base
Mistral AI
2025-03-17
GPT-4.1 nano
OpenAI
2025-04-14
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Pricing Comparison
Cost per million tokens (USD)
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GPT-4.1 nano
Input:$0.10
Output:$0.40
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Mistral Small 3.1 24B Base
Input:$0.10
Output:$0.30($0.10 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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GPT-4.1 nano
Average Score:61.9%(+2.7%)
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Mistral Small 3.1 24B Base
Average Score:59.3%
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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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GPT-4.1 nano
1 providers
OpenAI
Throughput: 200 tok/s
Latency: 2ms
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Mistral Small 3.1 24B Base
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GPT-4.1 nano
Avg Score:61.9%(+2.7%)
Providers:1
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Mistral Small 3.1 24B Base
Avg Score:59.3%
Providers:1