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Mistral Small 3.1 24B Base vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Mistral Small 3.1 24B Base leads with 4.2% higher average benchmark score. Both models have similar pricing. Both models have their strengths depending on your specific coding needs.
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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 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
1 month newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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-multimodal-instruct
Input:$0.05
Output:$0.10($0.25 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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Mistral Small 3.1 24B Base
Average Score:59.3%(+4.2%)
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Phi-4-multimodal-instruct
Average Score:55.1%
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Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
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-multimodal-instruct
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Mistral Small 3.1 24B Base
Avg Score:59.3%(+4.2%)
Providers:1
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Phi-4-multimodal-instruct
Avg Score:55.1%
Providers:1