mistral:latest

llama · 7.2B · Q4_0

Mac Mini (Apple M4 Pro)

64 GB · macOS 15.7.4

Tested on February 28, 2026

Top 17% Compare

Global Score

76 /100

Good

Hardware Fit

100/100

Quality

60/100

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Ollama

ollama pull mistral:latest

View on Ollama Library

ollama.com/library/mistral

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Find on HuggingFace

GGUF versions & quantizations

Hardware

Machine: Mac Mini
CPU: Apple M4 Pro
Cores: 14 total (10 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.7.4
Arch: arm64

Performance

Tokens/sec Tokens generated per second — higher is better: 54.3
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
Time to first token How fast the model starts responding — lower is better: 124 ms
Load time Time to load the model into memory before inference: 0.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 9.9 GB (15%)
Total tokens Total number of tokens generated across all benchmark prompts: 1213

Score breakdown

Speed

40/40

Time to first token

30/30

Memory

30/30

Quality

Reasoning

8/20

Coding

15/20

Instruction following

13/20

Structured output

12/15

Math

3/15

Multilingual

9/10

Category levels

Reasoning: Weak Coding: Adequate Instruction Following: Adequate Structured Output: Strong Math: Poor Multilingual: Strong

Metadata

Spec version: 0.1.0
Runtime: Ollama 0.17.4
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 62b8c3588c2d3110746db6e6bb1e75ae7380cb0bb2d03936b367519799d02a33

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 76/100). Category profile: Reasoning: Weak, Coding: Adequate, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

Run yours now

npx metrillm@latest bench

Requires Node 20+ and Ollama running