Model datasheet · Qwen3.5
Qwen3.5 397B-A17B
The largest model on this site and the top of the Qwen3.5 line. Gated DeltaNet plus sparse MoE: only 15 of its 60 layers are full attention, so 256K of context costs about 8GB of cache — remarkably cheap for its size. The weights are the problem, not the cache.
- Vendor
- Alibaba
- Architecture
- MoE · 397B total / ~17B active
- Context
- 262,144 tokens
- License
- Apache-2.0
- Released
- 2026-02-16
- Vision
- Yes
§1 Characteristics by quantization band
Q2–Q3 — 2–3 bit
177GB weights · e.g. IQ2_M , Q3_K_M + KV: 240MB at 8K · 960MB at 32K · 3.8GB at 128Kdata 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 MoE this large loses expert-routing precision first, which surfaces as dropped tool arguments and broken multi-file edits before prose visibly suffers.
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 MoE this large loses expert-routing precision first, which surfaces as dropped tool arguments and broken multi-file edits before prose visibly suffers.
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.
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.
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.
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.
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.
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.
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.
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)
793GB weights · e.g. bf16 + KV: 240MB at 8K · 960MB at 32K · 3.8GB at 128Kdata checked Aug 2026
- vendor-model-card — SWE-bench Verified = 76.4 @ bf16 · vendor-reported · 2026-08-31
LiveCodeBench v6 83.6, Terminal Bench 2 52.5. Beats GLM-4.7's 73.8 on SWE-bench.
data checked Aug 2026
- vendor-model-card — MMLU-Pro = 87.8 @ bf16 · vendor-reported · 2026-08-31
No hallucination benchmark published; this is a knowledge score, not a truthfulness one.
data checked Aug 2026
- vendor-model-card — IFEval = 92.6 @ bf16 · vendor-reported · 2026-08-31
IFBench 76.5. Both are program-checkable, so there is little interpretive room.
data checked Aug 2026
- vendor-model-card — AIME26 = 91.3 @ bf16 · vendor-reported · 2026-08-31
GPQA 88.4, MMLU-Pro 87.8 — the highest GPQA figure in this catalogue.
data checked Aug 2026
- vendor-model-card — BFCL-V4 = 72.9 @ bf16 · vendor-reported · 2026-08-31
Terminal Bench 2 52.5 — the best terminal-agent figure here, though BFCL trails GLM-4.7's Tau2 result.
§2 Known issues & what fixes them
The unusual shape of this model is that context is cheap and weights are not. Three of every four layers use linear attention, so the KV cache is only 30KB per token — a full 256K context costs about 8GB, less than GLM-4.7 spends on 32K. But the 4-bit weights are 244GB. Nothing about a long context will stop you here; holding the model will.
Architectural, and mostly good news. Size the machine for weights and treat context as close to free.
Evidence · 2 sources · community-consensus
- Qwen3.5-397B config.json: layer_types, 3 linear per 1 full attention across 60 layers (vendor, 2026-08-31)
- Unsloth Qwen3.5 run guide (blog, 2026-08-31)
Every number scored here is Alibaba's own. Qwen3.5-397B is a flagship release with headline figures that beat much larger closed models, and no independent replication of those figures exists in this dataset. Treat them as claims. Its factuality and long-context cells are blank rather than estimated because nobody has published a hallucination or RULER result for it.
Nothing to work around — this is a caveat on the evidence, not on the model.
Evidence · 2 sources · community-consensus
- Qwen3.5-397B-A17B model card: all benchmark figures are vendor-reported (vendor, 2026-08-31)
- unsloth GGUF quant ladder — community distribution, no independent benchmarks (other, 2026-08-31)