Is local/qwen3.8-flash-next good for coding?

In our benchmarks, local/qwen3.8-flash-next scores 49.6/100 for coding. It runs at about 102.7 tok/s, so if you want more speed, Qwen3.6-35B-A3B-Q4_K_XL is faster (~156.9 tok/s) and still scores well for coding (82.2/100).

Is local/qwen3.8-flash-next good for agentic (tool-using) tasks?

In our benchmarks, local/qwen3.8-flash-next scores 80.8/100 for agentic workflows. It runs at about 102.7 tok/s, so if you want more speed, swift-1.5-qwen3.8-flash-next-iq3_s is faster (~259.0 tok/s) and still scores well for agentic workflows (88.9/100).

How fast is qwen3.8-flash-next for local inference?

Across 5 community benchmark runs, qwen3.8-flash-next reaches up to 174.8 tok/s and averages 102.7 tok/s, with the fastest results on NVIDIA GeForce RTX 4090 D.

Which tools have been used to run qwen3.8-flash-next?

Benchmarks were submitted using llama.cpp, mlx-serve, vLLM. Results are community-contributed and updated as new runs arrive.

Model benchmarks

qwen3.8-flash-next local LLM performance

As of October 2026, qwen3.8-flash-next runs at up to 174.8 tok/s for local inference (best of 5 community benchmark runs across 3 GPUs).

llama.cppmlx-servevLLM

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Model size

Unknown

Peak speed

174.8 tok/s

Average speed

102.7 tok/s

Avg PP

1715.2 tok/s

Min memory

n/a

Max context

262,144 tokens

Avg output / run

31,357 tokens

Avg runtime / run

5m 39s

Avg quality

74.8

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 5 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	64.8	74.8	84.6
Agent Workflow	71.6	80.8	89.2
Code Generation	30.5	49.6	75.4
Role Play & Narrative	76.8	86.0	92.6
Research & Analysis	77.5	82.8	87.6

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.8-flash-next has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 4090 D	vLLM	—	174.8 tok/s	173.4 tok/s	n/a	262,144 tokens	77.7	2
Apple M3 Ultra	mlx-serve	—	74.7 tok/s	74.6 tok/s	n/a	8,192 tokens	66.3	2
NVIDIA TITAN RTX	llama.cpp	—	17.5 tok/s	17.5 tok/s	n/a	65,536 tokens	85.9	1

Benchmark runs

All 5 qwen3.8-flash-next runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is local/qwen3.8-flash-next good for coding?: In our benchmarks, local/qwen3.8-flash-next scores 49.6/100 for coding. It runs at about 102.7 tok/s, so if you want more speed, Qwen3.6-35B-A3B-Q4_K_XL is faster (~156.9 tok/s) and still scores well for coding (82.2/100).
Is local/qwen3.8-flash-next good for agentic (tool-using) tasks?: In our benchmarks, local/qwen3.8-flash-next scores 80.8/100 for agentic workflows. It runs at about 102.7 tok/s, so if you want more speed, swift-1.5-qwen3.8-flash-next-iq3_s is faster (~259.0 tok/s) and still scores well for agentic workflows (88.9/100).
How fast is qwen3.8-flash-next for local inference?: Across 5 community benchmark runs, qwen3.8-flash-next reaches up to 174.8 tok/s and averages 102.7 tok/s, with the fastest results on NVIDIA GeForce RTX 4090 D.
Which tools have been used to run qwen3.8-flash-next?: Benchmarks were submitted using llama.cpp, mlx-serve, vLLM. Results are community-contributed and updated as new runs arrive.