mistralai/magistral-small-2509

mistral3 · 24B · 4bit

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 4, 2026 · Submitted by Topaz750

Top 33% Compare

Global Score

74 /100

Good

Hardware Fit

66/100

Quality

78/100

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Hardware

Machine: MacBook Air
CPU: Apple M4
Cores: 10 total (4 perf + 6 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M4
OS: macOS 26.3
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 7.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.3
First chunk latency Delay before the first streamed chunk arrives from runtime: 1.4 s
Time to first token How fast the model starts responding — lower is better: 1.4 s
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 13.2 GB (41%)
Total tokens Total number of tokens generated across all benchmark prompts: 974

Score breakdown

Speed

16/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

14/20

Coding

18/20

Instruction following

14/20

Structured output

15/15

Math

7/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: LM Studio 0.4.6+1
Model format: MLX
Hardware profile: BALANCED
Result hash: 77ec67ffec734ca974e13d48fcddad6dd3a6aacb712fbc268484336b39cae6ba

Interpretation

Hardware fit: 66/100. Overall suitability: GOOD (Global 74/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 9% (peak 11%)

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$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest