Qwen3.6-35B-A3B

Language Alibaba Qwen Verified Released Apr 16, 2026
  • In Input modalities: TextImageVideo Out Output modalities: Text
  • Reasoning
  • Tool use
  • Structured output
  • Open weights

Prices

6 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official Global · USD qwen3.6-35b-a3b
Conditions/ 1M tokensInputOutput
Standard $0.248$1.485

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 qwen3.6-35b-a3b
Conditions/ 1M tokensInputOutput
Standard $0.375$2.25

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 qwen3.6-35b-a3b
Conditions/ 1M tokensInputOutput
Standard ¥1.80¥10.80

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 qwen3.6-35b-a3b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 21, 2026 $0.248$1.485

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

Reasoning can be off
—
Max thinking budget
131.07K
Built-in tools
Web search
Licence
apache-2.0
Family
qwen
Model ID

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

Run locally

Weights: Qwen/Qwen3.6-35B-A3B
  • Parameters 36B
  • Active 3B · 256 experts, 8 per token
  • Native precision BF16
  • Layers 40
  • KV cache per token 20 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
Runs well IQ4_XS (automatic) 8K context 286–429 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 18.8 GB (weights 17.8 + KV cache 0.2 + overhead 0.9)
Usable
21.6 GB (90% of its memory)
Generation speed
286–429 tok/s (reads the active 3B per token)
Longest context
149K (longer doesn’t fit)
First token
about 0.10–0.15 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file67.0 GB≈ 70.5Needs system memory 10–15 tok/s—
Q8_035.6 GB≈ 37.5Needs system memory 34–51 tok/s—
FP8maker’s file34.9 GB≈ 36.8Needs system memory 35–52 tok/s—
Q6_K27.5 GB≈ 29.0Needs system memory 54–80 tok/s—
Q5_K_M23.8 GB≈ 25.2Needs system memory 93–140 tok/s—
Q5_023.0 GB≈ 24.3Needs system memory 107–161 tok/s—
Q4_K_M20.3 GB≈ 21.5Runs well 253–380 tok/s12K
Q4_018.8 GB≈ 19.9Runs well 272–407 tok/s93K
IQ4_XS17.8 GB≈ 18.8Runs well 286–429 tok/s149K
Q3_K_Mquality drops16.4 GB≈ 17.3Runs well 308–463 tok/s226K
IQ3_XXSquality drops12.8 GB≈ 13.6Runs well 383–575 tok/s256K
Q2_Kquality drops12.6 GB≈ 13.3Runs well 390–585 tok/s256K
IQ2_XXSquality drops8.6 GB≈ 9.3Runs well 536–804 tok/s256K

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): GPU GeForce RTX 4090; unified memory Apple M5 Max (32-core GPU) · 36 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≈ 538.6 GBDoesn’t fit your hardware
LoRA≈ 70.0 GBDoesn’t fit your hardware
QLoRA≈ 21.8 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 144

    #84 of 118 listed models · CI 142–145.9

  • Epoch AI · FrontierMath Tiers 1–3 20.4%

    #71 of 84 listed models · reasoning off · ±2.39

    Also listed: qwen3.6-35b-a3b 17.5%

  • Epoch AI · GPQA Diamond 84.8%

    #68 of 168 listed models · reasoning off · ±2.55

    Also listed: qwen3.6-35b-a3b 83.8%

  • Epoch AI · Mystery Game Puzzles 22%

    #41 of 77 listed models · reasoning off · ±4.16

  • 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
  • changes we made

Provider-reported scores

Published by Alibaba Qwen, under the conditions it states
BenchmarkSettingScore
SWE-bench Verifiedinternal agent scaffold, 200K context73.4%
Terminal-Bench 2.0Terminus-2 harness, avg of 5 runs51.5%
MMLU-Prothinking mode85.2%
GPQA Diamondthinking mode86%
Humanity's Last Examno tools, thinking mode21.4%
LiveCodeBenchv6, thinking mode80.4%
AIME 2026full AIME 2026 (I & II), thinking mode92.7%
MMMUthinking mode81.7%
  1. 1 price dropped on Alibaba Cloud Model Studio Output (Reasoning on) $1.485
  2. 2 prices dropped on Alibaba Cloud Model Studio, International Output (Reasoning off) $2.25 · Output (Reasoning on) $2.25

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