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Mistral Small 3.1 24B Base vs Phi 4
Comprehensive side-by-side LLM comparison
Phi 4 leads with 12.3% higher average benchmark score. Mistral Small 3.1 24B Base offers 224.0K more tokens in context window than Phi 4. Both models have similar pricing. Mistral Small 3.1 24B Base supports multimodal inputs. Overall, Phi 4 is the stronger choice for coding tasks.
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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.
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Microsoft
Phi-4 was introduced as the fourth generation of Microsoft's small language model series, designed to push the boundaries of what compact models can achieve. Built with advanced training techniques and architectural improvements, it demonstrates continued progress in efficient, high-quality language models.
3 months newer
Phi 4
Microsoft
2024-12-12
Mistral Small 3.1 24B Base
Mistral AI
2025-03-17
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Pricing Comparison
Cost per million tokens (USD)
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Mistral Small 3.1 24B Base
Input:$0.10
Output:$0.30
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Phi 4
Input:$0.07
Output:$0.14($0.19 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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Mistral Small 3.1 24B Base
Average Score:58.2%
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Phi 4
Average Score:70.4%(+12.3%)
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Knowledge Cutoff
Training data recency comparison
Phi 4
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Mistral Small 3.1 24B Base
1 providers
Mistral AI
Throughput: 137.1 tok/s
Latency: 0.23ms
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Phi 4
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Mistral Small 3.1 24B Base
Avg Score:58.2%
Providers:1
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Phi 4
Avg Score:70.4%(+12.3%)
Providers:1