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Mistral 7B vs Phi-4

Mistral
Mistral 7B

Mistral AI · 7.3B params · Quality: 56

Microsoft
Phi-4

Microsoft · 14.7B params · Quality: 83

Architecture Comparison

SpecMistral 7BPhi-4
TypeDENSEDENSE
Total Parameters7.3B14.7B
Active Parameters7.3B14.7B
Layers3240
Hidden Dimension4,0965,120
Attention Heads3240
KV Heads810
Context Length32,76816,384
Precision (default)BF16BF16

Memory Requirements

PrecisionMistral 7BPhi-4
BF16 Weights14.6 GB29.4 GB
FP8 Weights7.3 GB14.7 GB
INT4 Weights3.6 GB7.3 GB
KV-Cache / Token131072 B204800 B
Activation Estimate1.00 GB1.50 GB

Minimum GPUs Needed (BF16)

H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU

Quality Benchmarks

BenchmarkMistral 7BPhi-4
Overall5683
MMLU62.584.8
HumanEval32.067.0
GSM8K52.293.0
MT-Bench71.085.0

Mistral 7B

MMLU
62.5
HumanEval
32.0
GSM8K
52.2
MT-Bench
71.0

Phi-4

MMLU
84.8
HumanEval
67.0
GSM8K
93.0
MT-Bench
85.0

Capabilities

FeatureMistral 7BPhi-4
Tool Use✗ No✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✓ Yes
Multilingual✓ Yes✓ Yes
Structured Output✗ No✓ Yes

API Pricing Comparison

Cheapest Output (Mistral 7B)

$0.07/M

Input: $0.07/M

Cheapest Output (Phi-4)

$0.14/M

Input: $0.07/M

ProviderMistral 7B In $/MOut $/MPhi-4 In $/MOut $/M
deepinfra$0.07$0.07
azure$0.07$0.14
together$0.20$0.20$0.20$0.20

Recommendation Summary

  • Phi-4 scores higher on overall quality (83 vs 56).
  • Mistral 7B is cheaper per output token ($0.07/M vs $0.14/M).
  • Mistral 7B has a smaller memory footprint (14.6 GB vs 29.4 GB BF16), making it easier to deploy on fewer GPUs.
  • Mistral 7B supports a longer context window (32,768 vs 16,384 tokens).
  • Phi-4 is stronger at code generation (HumanEval: 67.0 vs 32.0).
  • Phi-4 is better at math reasoning (GSM8K: 93.0 vs 52.2).

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