Is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp good for coding?

In our benchmarks, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp scores 62.2/100 for coding. It runs at about 47.8 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).

Is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp good for agentic (tool-using) tasks?

In our benchmarks, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp scores 89.3/100 for agentic workflows. It runs at about 47.8 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).

How fast is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp for local inference?

Across 14 community benchmark runs, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp reaches up to 50.8 tok/s and averages 47.8 tok/s, with the fastest results on Apple M4 Max.

How much memory does Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp need?

The leanest observed configuration used about 81.8 GB of memory.

Which tools have been used to run Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp?

Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.

Model benchmarks

Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp local LLM performance

As of September 2026, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp runs at up to 50.8 tok/s for local inference (best of 14 community benchmark runs across 1 GPU).

oMLX

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Model size

Peak speed

50.8 tok/s

Average speed

47.8 tok/s

Avg PP

234.3 tok/s

Min memory

81.8 GB

Max context

262,144 tokens

Avg output / run

48,988 tokens

Avg runtime / run

18m 32s

Avg quality

82.1

Benchmark runs

GPUs tested

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 14 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	72.2	82.1	87.2
Agent Workflow	81.8	89.3	94.5
Code Generation	34.7	62.2	77.7
Role Play & Narrative	81.8	90.3	95.0
Research & Analysis	82.7	86.6	91.0

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
Apple M4 Max	oMLX	—	50.8 tok/s	47.8 tok/s	81.8 GB	262,144 tokens	82.1	14

Benchmark runs

All 14 Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp good for coding?: In our benchmarks, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp scores 62.2/100 for coding. It runs at about 47.8 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).
Is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp good for agentic (tool-using) tasks?: In our benchmarks, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp scores 89.3/100 for agentic workflows. It runs at about 47.8 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
How fast is Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp for local inference?: Across 14 community benchmark runs, Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp reaches up to 50.8 tok/s and averages 47.8 tok/s, with the fastest results on Apple M4 Max.
How much memory does Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp need?: The leanest observed configuration used about 81.8 GB of memory.
Which tools have been used to run Qwen3.8-Flash-Next-Dynamic-oQ4e-Q4GU-g64-BF16-PLE-MLX-mtp?: Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.