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

Model datasheet · GLM

GLM-4.7-Flash

The agentic-coding MoE of the moment: SWE-bench 59.2 from 3B active params, MIT-licensed, ~200K context. Watch it at long context, where its tool calls start fraying.

Vendor
Z.ai
Architecture
MoE · 30B total / ~3B active
Context
202,752 tokens
License
MIT
Released
2026-01-19
Vision
No

→ GGUF (llama.cpp / LM Studio / Ollama) → MLX (Apple Silicon)

§1 Characteristics by quantization band

Q2–Q3 — 2–3 bit

15GB weights · e.g. Q3_K_M + KV: 423MB at 8K · 1.7GB at 32K · 6.6GB at 128K

— Not yet rated · contributions welcome —

Q4–Q5 — 4–5 bit

18GB weights · e.g. Q4_K_M , mlx-4bit + KV: 423MB at 8K · 1.7GB at 32K · 6.6GB at 128K
Math & reasoning 7/10
data checked Aug 2026
  • quant-degradation-community — KLD/perplexity delta vs bf16 @ Q4_K_M · aggregated · 2026-08-30

    Editorial derivation: MoE expert weights lose a little at 4-bit.

Coding 6/10
data checked Aug 2026
  • quant-degradation-community — KLD/perplexity delta vs bf16 @ Q4_K_M · aggregated · 2026-08-30

    Editorial derivation: small loss at 4-bit.

Tool calling / agentic 6/10
data checked Aug 2026
  • quant-degradation-community — community consensus @ Q4_K_M · aggregated · 2026-08-30

    Editorial derivation: quant plus long context compounds the tool-call fraying — keep context modest at 4-bit.

Q6–Q8 — 6–8 bit

25GB weights · e.g. Q6_K + KV: 423MB at 8K · 1.7GB at 32K · 6.6GB at 128K

— Not yet rated · contributions welcome —

FP16 — full precision (fp16/bf16)

60GB weights · e.g. bf16 + KV: 423MB at 8K · 1.7GB at 32K · 6.6GB at 128K
Math & reasoning 8/10
data checked Aug 2026
Coding 7/10
data checked Aug 2026
Tool calling / agentic 7/10
data checked Aug 2026
  • vendor-model-card — Tau2-Bench = 79.5 @ bf16 · vendor-reported · 2026-08-30

    Strong at short context; see the known issue on long-context degradation.

Factuality 4/10
data checked Aug 2026

§2 Known issues & what fixes them

ISSUE-01 moderate Q2–Q3 / Q4–Q5 / Q6–Q8 / FP16 · tool-calling, long-context

Output quality and tool-call formatting degrade as context grows — malformed JSON and artifacts appear well before the ~200K window is full.

Fix
Update your runtime [partial]

Recent llama.cpp builds fixed part of this (flash-attention path); update first.

Fix
Prompt technique [partial]

Cap working context well below the maximum and summarize long agent sessions periodically.

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

Hallucinates more than its peers (HHEM 9.3% vs 5–7% for Qwen3/Gemma 3) — the price of its agentic tuning.

Fix
RAG (retrieval) [strong]

Ground factual work; keep it in its lane (code and agents).

Evidence · 1 source · anecdotal