Qwen3.5-122B-A10B

Language Alibaba Qwen Partly verified Released Feb 23, 2026
  • In Input modalities: TextImageVideo Out Output modalities: Text
  • Reasoning
  • Reasoning can be off
  • Tool use
  • Structured output
  • Open weights

Prices

6 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official Global · USD qwen3.5-122b-a10b
Conditions/ 1M tokensInputOutput
Standard $0.115$0.917
Prompts over 128K tokens $0.287$2.294

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 qwen3.5-122b-a10b
Conditions/ 1M tokensInputOutput
Standard $0.40$3.20

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 qwen3.5-122b-a10b
Conditions/ 1M tokensInputOutput
Standard ¥0.80¥6.40
Prompts over 128K tokens ¥2.00¥16.00

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 qwen3.5-122b-a10b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 21, 2026 $0.115$0.917
Prompts over 128K tokens no longer listed since Oct 21, 2026 $0.287$2.294

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

Built-in tools
Web search
Licence
apache-2.0
Family
qwen
Model ID

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

Run locally

Weights: Qwen/Qwen3.5-122B-A10B
  • Parameters 125.1B
  • Active 10B · 256 experts, 8 per token
  • Native precision BF16
  • Layers 48
  • KV cache per token 24 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
Needs system memory IQ4_XS (automatic) 8K context 11–17 tok/s34/48 layers in system memory
WeightsKV cacheOverheadIn system memoryUsable
Needs
≈ 65.2 GB (weights 61.9 + KV cache 0.2 + overhead 3.1)
Usable
21.6 GB (90% of its memory)
In system memory
43.8 GB · 14/48 layers on the GPU (--n-gpu-layers 14)
Generation speed
11–17 tok/s (reads the active 10B per token)
First token
about 0.32–0.48 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file233.0 GB≈ 244.8Doesn’t fit—
Q8_0123.8 GB≈ 130.2Doesn’t fit—
FP8maker’s file118.4 GB≈ 124.5Doesn’t fit—
Q6_K95.7 GB≈ 100.6Doesn’t fit—
Q5_K_M82.9 GB≈ 87.2Doesn’t fit—
Q5_080.1 GB≈ 84.3Doesn’t fit—
Q4_K_M70.8 GB≈ 74.5Needs system memory 10–14 tok/s—
Q4_065.5 GB≈ 69.0Needs system memory 11–16 tok/s—
INT4 (AWQ / GPTQ)maker’s file73.4 GB≈ 77.3Needs system memory 9–13 tok/s—
IQ4_XS61.9 GB≈ 65.2Needs system memory 11–17 tok/s—
Q3_K_Mquality drops56.9 GB≈ 60.0Needs system memory 21–32 tok/s—
IQ3_XXSquality drops44.6 GB≈ 47.0Needs system memory 27–41 tok/s—
Q2_Kquality drops43.7 GB≈ 46.1Needs system memory 28–42 tok/s—
IQ2_XXSquality drops30.0 GB≈ 31.7Needs system memory 42–63 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 RTX PRO 6000 Blackwell Workstation Edition; unified memory Apple M5 Max (40-core GPU) · 128 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≈ 1867.1 GBDoesn’t fit your hardware
LoRA≈ 236.5 GBDoesn’t fit your hardware
QLoRA≈ 69.0 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
  • LMArena Text 1,417

    #86 of 163 listed models · CI 1,412–1,421 · 29,712 votes

  • Epoch AI · Mystery Game Puzzles 17%

    #54 of 77 listed models · reasoning off · ±3.78

  • EQ-Bench Longform Writing 54.3

    #72 of 92 listed models

  • LMArena Text · Chinese 1,458

    #80 of 161 listed models · CI 1,444–1,471 · 2,123 votes

  • LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Oct 19 – 28, 2026
  • 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, 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
  1. Context window 256K → 262.14K
  2. 2 new prices on SiliconFlow, China Input ¥0.80 · Output ¥6.40
  3. 1 price dropped on Alibaba Cloud Model Studio Output (Reasoning on) $0.917
  4. 2 prices dropped on Alibaba Cloud Model Studio, International Output (Reasoning off) $3.20 · Output (Reasoning on) $3.20

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

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