Known-issues sheets for local language models Rev 0.1.0 · reviewed every 7 days

Model datasheet · Qwen3-Next

Qwen3-Coder-Next 80B-A3B

Three billion active parameters out of eighty, and 70.6 on SWE-bench Verified — within striking distance of models that activate ten times as much. The 49GB 4-bit build is the cheapest way onto this site's upper tier, and the sparsity that makes it fast is also what makes it fragile at low quant.

Vendor
Alibaba
Architecture
MoE · 80B total / ~3B active
Context
262,144 tokens
License
Apache-2.0
Released
2026-02-03
Vision
No

→ GGUF (llama.cpp / LM Studio / Ollama)

§1 Characteristics by quantization band

Q2–Q3 — 2–3 bit

38.3GB weights · e.g. Q3_K_M + KV: 192MB at 8K · 768MB at 32K · 3GB at 128K
Coding 5/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q3_K_M · aggregated · 2026-08-31

    Editorial derivation: two points down. At 3-bit a sparse MoE loses expert-routing precision first, which surfaces as dropped tool arguments and broken multi-file edits before prose visibly suffers.

Tool calling / agentic 4/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q3_K_M · aggregated · 2026-08-31

    Editorial derivation: two points down. At 3-bit a sparse MoE loses expert-routing precision first, which surfaces as dropped tool arguments and broken multi-file edits before prose visibly suffers.

Q4–Q5 — 4–5 bit

49.3GB weights · e.g. Q4_K_M + KV: 192MB at 8K · 768MB at 32K · 3GB at 128K
Coding 7/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q4_K_M · aggregated · 2026-08-31

    Editorial derivation: one point down from the measured bf16 figure. 4-bit is the band a model this size is actually run at, and the loss is real but modest.

Tool calling / agentic 6/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q4_K_M · aggregated · 2026-08-31

    Editorial derivation: one point down from the measured bf16 figure. 4-bit is the band a model this size is actually run at, and the loss is real but modest.

Q6–Q8 — 6–8 bit

65.6GB weights · e.g. Q6_K + KV: 192MB at 8K · 768MB at 32K · 3GB at 128K
Coding 8/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31

    Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size — the differences that show up at 4-bit and below are not measurable here. Recorded explicitly rather than left to band fallback, which would otherwise borrow the 4-bit number and subtract a point.

Tool calling / agentic 7/10
data checked Aug 2026
  • quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31

    Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size — the differences that show up at 4-bit and below are not measurable here. Recorded explicitly rather than left to band fallback, which would otherwise borrow the 4-bit number and subtract a point.

FP16 — full precision (fp16/bf16)

159GB weights · e.g. bf16 + KV: 192MB at 8K · 768MB at 32K · 3GB at 128K
Coding 8/10
data checked Aug 2026
  • vendor-model-cardSWE-bench Verified = 70.6 @ bf16 · vendor-reported · 2026-08-31

    SWE-bench Pro 44.3. Qwen claim performance comparable to models with 10-20x more active parameters, and on SWE-bench Verified the figure supports it.

Tool calling / agentic 7/10
data checked Aug 2026
  • vendor-model-cardTerminal-Bench 2.0 = 36.2 @ bf16 · vendor-reported · 2026-08-31

    Below GLM-4.7's 41.0 and well below Qwen3.5-397B's 52.5 — the gap that 3B active parameters buys you.

§2 Known issues & what fixes them

ISSUE-01 moderate Q2–Q3 · tool-calling, coding

Only 3B of 80B parameters are active per token, routed across 512 experts. That extreme sparsity is what makes the model fast and small to run, and it is also what makes 3-bit risky: every routing decision is made on a thin slice of the network, and quantization noise there costs more than it would in a dense model of the same footprint.

Fix
Move up a quant band [strong]

Run the 49.3GB 4-bit build. Unsloth's own guidance is more than 45GB of unified memory or RAM/VRAM for 4-bit, which is a modest ask for this tier.

Evidence · 2 sources · community-consensus
ISSUE-02 moderate Q2–Q3 / Q4–Q5 / Q6–Q8 / FP16 · math, factuality, instruction-following

This is a coding model and its published evidence is coding-only: SWE-bench Verified, SWE-bench Pro and Terminal-Bench, and nothing else. There is no AIME, GPQA, MMLU, IFEval or hallucination figure for it, so five of the seven dimensions on this site are genuinely unmeasured rather than merely low. The coverage discount on its cards reflects that, and it should — do not read the blank cells as strength.

No fix
No known fix — choose a different model

Use it for what it was measured on. For general work, a Qwen3.5 model of similar size has evidence across the board.

Evidence · 2 sources · community-consensus