qwen3.5-9b-mlx
qwen3_5 · 9B · 4bit
THINKING MODEL
Mac mini (Apple M4)
24 GB · macOS 26.3
Tested on March 11, 2026 · Submitted by cryptoepops
Global Score
59 /100
Marginal
Hardware Fit
65/100
Quality
57/100
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Hardware
- Machine
- Mac mini
- CPU
- Apple M4
- Cores
- 10 total (4 perf + 6 eff)
- Frequency
- 2.4 GHz
- RAM
- 24 GB LPDDR5
- GPU
- Apple M4
- OS
- macOS 26.3
- Arch
- arm64
- Power Mode
- balanced
Performance
- Tokens/sec
- 14.3
- Standard deviation
- ±0.2
- First chunk latency
- 24 ms
- Time to first token
- 13.4 s
- Load time
- 13.3 s
- Memory usage
- 2.5 GB (10%)
- Total tokens
- 1617
- Thinking tokens (est.)
- ~614
Score breakdown
Speed
32/50
Time to first token
3/20
Memory
30/30
Quality
Reasoning
10/20
Coding
17/20
Instruction following
5/20
Structured output
9/15
Math
11/15
Multilingual
5/10
Category levels
Reasoning: Weak Coding: Strong Instruction Following: Weak Structured Output: Adequate Math: Adequate Multilingual: Adequate
Metadata
- Spec version
- 0.2.1
- Runtime
- LM Studio 0.4.6+1
- Model format
- MLX
- Hardware profile
- ENTRY
- Result hash
- f0e821ad6d4b50ee5fac6fe21f62611e4fa4b97cf081273c9274fd12882ff5dd
Interpretation
Hardware fit: 65/100. Overall suitability: MARGINAL (Global 59/100). Category profile: Reasoning: Weak, Coding: Strong, Instruction Following: Weak, Structured Output: Adequate, Math: Adequate, Multilingual: Adequate.
Warnings
- Token throughput is estimated from LM Studio output because native token stats were unavailable. Compare tok/s across backends cautiously.
Bench Environment
Power: AC CPU load: avg 19% (peak 23%)
Run yours now
$
npm install -g metrillm@latest$
metrillmRequires Node 20+ and Ollama or LM Studio running
Or run without installing: npx metrillm@latest