Qwen3-235B-A22B

Language Alibaba Qwen Partly verified Released Apr 29, 2025
  • In Input modalities: Text Out Output modalities: Text
  • Reasoning
  • Reasoning can be off
  • Tool use
  • Structured output
  • Open weights

Prices

3 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official International · USD qwen3-235b-a22b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.70$2.80
Reasoning off no longer listed since Oct 11, 2026 —$2.80
Reasoning on no longer listed since Oct 11, 2026 —$8.40

Prices from qwen3-235b-a22b model (opens in a new tab) (checked Oct 5, 2026) · Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official Global · USD qwen3-235b-a22b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.287$1.147
Reasoning off no longer listed since Oct 11, 2026 —$1.147
Reasoning on no longer listed since Oct 11, 2026 —$2.868

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026) · qwen3-235b-a22b model (opens in a new tab) (checked Oct 5, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3-235b-a22b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥2.00—
Reasoning off no longer listed since Oct 11, 2026 —¥8.00
Reasoning on no longer listed since Oct 11, 2026 —¥20.00

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Oct 6, 2026)

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

Details

Max thinking budget
38.91K
Knowledge cutoff
Apr 2025
Family
qwen
Model ID

Not stated on Alibaba Qwen’s pages: max input, reasoning efforts, built-in tools, licence, aliases.

Run locally

Weights: Qwen/Qwen3-235B-A22B
  • Parameters 235.1B
  • Active 22B · 128 experts, 8 per token
  • Native precision BF16
  • Layers 94
  • KV cache per token 188 KB
  • Native context 32K (model card)
  • With YaRN 128K (model card)
  • Config positions 40K (config.json max_position_embeddings)
  • API context 128K (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
≈ 123.6 GB (weights 116.3 + KV cache 1.5 + overhead 5.8)
Usable
21.6 GB (90% of its memory)
Try
IQ2_XXS runs (needs system memory), but below 4 bits quality drops noticeably
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file437.9 GB≈ 461.3Doesn’t fit—
Q8_0maker’s file232.8 GB≈ 245.9Doesn’t fit—
FP8maker’s file222.6 GB≈ 235.2Doesn’t fit—
Q6_Kmaker’s file179.8 GB≈ 190.2Doesn’t fit—
Q5_K_Mmaker’s file155.4 GB≈ 164.6Doesn’t fit—
Q5_0maker’s file150.8 GB≈ 159.8Doesn’t fit—
Q4_K_Mmaker’s file132.4 GB≈ 140.5Doesn’t fit—
Q4_0123.2 GB≈ 130.8Doesn’t fit—
INT4 (AWQ / GPTQ)maker’s file116.0 GB≈ 123.3Doesn’t fit—
IQ4_XS116.3 GB≈ 123.6Doesn’t fit—
Q3_K_Mquality drops107.0 GB≈ 113.8Doesn’t fit—
IQ3_XXSquality drops83.7 GB≈ 89.4Doesn’t fit—
Q2_Kquality drops82.1 GB≈ 87.7Doesn’t fit—
IQ2_XXSquality drops56.4 GB≈ 60.7Needs system memory 12–18 tok/s—

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 NVIDIA B200 (HGX B200); unified memory Apple M5 Ultra (64-core GPU) · 256 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≈ 3506.6 GBDoesn’t fit your hardware
LoRA≈ 442.0 GBDoesn’t fit your hardware
QLoRA≈ 127.3 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 138.4

    #101 of 118 listed models · CI 136–140.8

  • LMArena Text 1,375

    #115 of 163 listed models · CI 1,370–1,380 · 25,950 votes

  • Epoch AI · GPQA Diamond 70.7%

    #95 of 168 listed models · ±2.71

  • Epoch AI · MATH Level 5 68.9%

    #26 of 64 listed models · ±0.95

  • Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Oct 18 – Nov 5, 2026
  • LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Oct 28, 2026
  • changes we made

No changes recorded yet.

Sources

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

Some values on this page are still waiting to be checked against official pages: a filled dot is verified, a hollow one is not yet (with or without a source on file). How we verify data

Alibaba Qwen · China · qwen.ai

Snapshots, the same family and other models from this provider