Qwen3.5-397B-A17B

Language Alibaba Qwen Verified Released Feb 15, 2026
  • In Input modalities: TextImageVideo Out Output modalities: Text
  • Reasoning
  • Reasoning can be off
  • Tool use
  • Structured output
  • Open weights

Prices

5 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official Global · USD qwen3.5-397b-a17b
Conditions/ 1M tokensInputOutput
Standard $0.172$1.032
Prompts over 128K tokens $0.43$2.58

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Nov 2, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official International · USD qwen3.5-397b-a17b
Conditions/ 1M tokensInputOutput
Standard $0.60$3.60

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Nov 2, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3.5-397b-a17b
Conditions/ 1M tokensInputOutput
Standard ¥1.20¥7.20
Prompts over 128K tokens ¥3.00¥18.00

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Nov 2, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official Germany (global scope) · US (global scope) · USD qwen3.5-397b-a17b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 21, 2026 $0.172$1.032
Prompts over 128K tokens no longer listed since Oct 21, 2026 $0.43$2.58

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Nov 2, 2026)

Standard: the price a request with no conditions pays (the headline price elsewhere on the site).

Details

Max thinking budget
81.92K
Built-in tools
Web search
Licence
apache-2.0
Family
qwen
Model ID

Not stated on Alibaba Qwen’s pages: reasoning efforts, knowledge cutoff, aliases.

Run locally

Weights: Qwen/Qwen3.5-397B-A17B
  • Parameters 403.4B
  • Active 17B · 512 experts, 10 per token
  • Native precision BF16
  • Layers 60
  • KV cache per token 30 KB
  • Native context 256K (model card)
  • Config positions 256K (config.json max_position_embeddings)
  • API context 256K (maker’s API)
My hardware GeForce RTX 4090 24 GB · 1008 GB/s · 64 GB DDR5 dual channel · 89.6 GB/s
On your hardware · GeForce RTX 4090
Doesn’t fit IQ4_XS (automatic) 8K context
WeightsKV cacheOverheadUsable
Needs
≈ 209.8 GB (weights 199.6 + KV cache 0.2 + overhead 10.0)
Usable
21.6 GB (90% of its memory)
Try
it’s too large for this hardware; see the smallest that runs it below
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file751.4 GB≈ 789.2Doesn’t fit—
Q8_0399.2 GB≈ 419.4Doesn’t fit—
FP8maker’s file378.3 GB≈ 397.4Doesn’t fit—
Q6_K308.5 GB≈ 324.2Doesn’t fit—
Q5_K_M267.2 GB≈ 280.8Doesn’t fit—
Q5_0258.3 GB≈ 271.4Doesn’t fit—
Q4_K_M228.2 GB≈ 239.9Doesn’t fit—
Q4_0211.3 GB≈ 222.1Doesn’t fit—
INT4 (AWQ / GPTQ)maker’s file219.5 GB≈ 230.7Doesn’t fit—
IQ4_XS199.6 GB≈ 209.8Doesn’t fit—
Q3_K_Mquality drops183.6 GB≈ 193.0Doesn’t fit—
IQ3_XXSquality drops143.7 GB≈ 151.1Doesn’t fit—
Q2_Kquality drops140.9 GB≈ 148.2Doesn’t fit—
IQ2_XXSquality drops96.7 GB≈ 101.8Doesn’t fit—

Click a row to choose a precision. “Maker’s file” sizes are the maker’s own files; others are parameters × bits per weight.

Compared with other hardware

Each at the highest precision it runs. Click one for details.

Smallest that runs it (Q4_K_M, 8K): no single listed GPU; unified memory Apple M5 Ultra (80-core GPU) · 512 GB.

Run commands

The maker publishes no files at this precision. Choose its native precision or one of its own builds, or quantise it yourself with llama.cpp.

Memory to fine-tune
Full≈ 6014.7 GBDoesn’t fit your hardware
LoRA≈ 755.5 GBDoesn’t fit your hardware
QLoRA≈ 215.4 GBDoesn’t fit your hardware

Batch 1, 2,048-token sequences, gradient checkpointing; LoRA and QLoRA train rank-16 adapters on the attention projections.

Estimates; facts from the maker’s Hugging Face repositories, as of 2026-11-06. How it’s estimated

Cited scores

From third-party leaderboards, not measured by us
  • Epoch Capabilities Index 147.9

    #65 of 118 listed models · CI 145.7–150

  • LMArena Text 1,441

    #60 of 163 listed models · CI 1,438–1,445 · 97,675 votes

  • Epoch AI · FrontierMath Tiers 1–3 31.2%

    #65 of 84 listed models · reasoning off · ±2.75

    Also listed: qwen3.5-397b-a17b 29.5%

  • Epoch AI · GPQA Diamond 86.4%

    #61 of 168 listed models · reasoning off · ±2.45

    Also listed: qwen3.5-397b-a17b 85.9%

  • Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Oct 28 – Nov 5, 2026
  • LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Oct 19 – 28, 2026
  • EQ-Bench by Samuel J. Paech (eqbench.com; EQ-bench/EQ-bench-site on GitHub), MIT License. · License · as of Oct 28, 2026
  • changes we made

Provider-reported scores

Published by Alibaba Qwen, under the conditions it states
BenchmarkSettingScore
MMLU-Prothinking mode87.8%
GPQA Diamondthinking mode88.4%
Humanity's Last Examno tools, thinking mode28.7%
with tools (search agent, context folding)48.3%
LiveCodeBenchv6, thinking mode83.6%
τ²-benchairline domain with Claude Opus 4.5 system-card fixes86.7%
BrowseCompsimple context folding69%
discard-all context strategy78.6%
SWE-bench Verifiedthinking mode76.4%
Terminal-Bench 2.0thinking mode52.5%
MMMUthinking mode85%
  1. Context window 256K → 262.14K
  2. 1 price dropped on Alibaba Cloud Model Studio Output (Reasoning on) $1.032
  3. 2 prices dropped on Alibaba Cloud Model Studio, International Output (Reasoning off) $3.60 · Output (Reasoning on) $3.60

Sources

Alibaba Qwen’s official policies: data use, privacy, terms →

Alibaba Qwen · China · qwen.ai

Snapshots, the same family and other models from this provider