Is qwen3_coder_next good for coding?

In our benchmarks, qwen3_coder_next scores 67.7/100 for coding. It runs at about 37.9 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_coder_next good for agentic (tool-using) tasks?

In our benchmarks, qwen3_coder_next scores 82.1/100 for agentic workflows. It runs at about 37.9 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_coder_next for local inference?

Across 10 community benchmark runs, qwen3_coder_next reaches up to 76.9 tok/s and averages 37.9 tok/s, with the fastest results on Apple M5 Max.

How much memory does qwen3_coder_next need?

The leanest observed configuration used about 50.3 GB of memory (quantizations tested: Q4_K_M, Q4_K_S).

Which tools have been used to run qwen3_coder_next?

Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.

Model benchmarks

qwen3_coder_next local LLM performance

As of October 2026, qwen3_coder_next runs at up to 76.9 tok/s for local inference (best of 10 community benchmark runs across 3 GPUs).

LM StudioOllamaQ4_K_MQ4_K_S

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Model size

79.7B

Peak speed

76.9 tok/s

Average speed

37.9 tok/s

Avg PP

436.1 tok/s

Min memory

50.3 GB

Max context

65,536 tokens

Avg output / run

8,746 tokens

Avg runtime / run

5m 21s

Avg quality

77.1

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 10 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	70.9	77.1	82.3
Agent Workflow	66.3	82.1	89.3
Code Generation	58.0	67.7	74.7
Role Play & Narrative	75.4	86.7	94.4
Research & Analysis	58.6	72.1	81.6

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3_coder_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
Apple M5 Max	LM Studio	Q4_K_S	76.9 tok/s	76.9 tok/s	51.2 GB	65,536 tokens	78.5	1
Apple M4 Max	LM Studio	—	51.9 tok/s	51.9 tok/s	n/a	65,536 tokens	76.6	1
Apple M5 Max	Ollama	Q4_K_M	34.1 tok/s	31.4 tok/s	50.3 GB	65,536 tokens	73.9	4
NVIDIA GeForce RTX 3080	LM Studio	—	31.2 tok/s	31.2 tok/s	n/a	8,192 tokens	80.1	4

Benchmark runs

All 10 qwen3_coder_next runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is qwen3_coder_next good for coding?: In our benchmarks, qwen3_coder_next scores 67.7/100 for coding. It runs at about 37.9 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_coder_next good for agentic (tool-using) tasks?: In our benchmarks, qwen3_coder_next scores 82.1/100 for agentic workflows. It runs at about 37.9 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_coder_next for local inference?: Across 10 community benchmark runs, qwen3_coder_next reaches up to 76.9 tok/s and averages 37.9 tok/s, with the fastest results on Apple M5 Max.
How much memory does qwen3_coder_next need?: The leanest observed configuration used about 50.3 GB of memory (quantizations tested: Q4_K_M, Q4_K_S).
Which tools have been used to run qwen3_coder_next?: Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.