Qwen3-30B-A3B

Language Alibaba Qwen 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

4 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official International · USD qwen3-30b-a3b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.20$0.80
Reasoning off no longer listed since Oct 11, 2026 —$0.80
Reasoning on no longer listed since Oct 11, 2026 —$2.40

Prices from qwen3-30b-a3b model (opens in a new tab) (checked Oct 7, 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 qwen3-30b-a3b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.108$0.431
Reasoning off no longer listed since Oct 11, 2026 —$0.431
Reasoning on no longer listed since Oct 11, 2026 —$1.076

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026) · qwen3-30b-a3b model (opens in a new tab) (checked Oct 5, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3-30b-a3b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥0.75—
Reasoning off no longer listed since Oct 11, 2026 —¥3.00
Reasoning on no longer listed since Oct 11, 2026 —¥7.50

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

Licence
apache-2.0
Family
qwen
Model ID

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

Run locally

Weights: Qwen/Qwen3-30B-A3B
  • Parameters 30.5B
  • Active 3.3B · 128 experts, 8 per token
  • Native precision BF16
  • Layers 48
  • KV cache per token 96 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
Runs well Q4_K_M (automatic) 8K context 179–269 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 18.9 GB (weights 17.3 (maker’s file) + KV cache 0.8 + overhead 0.9)
Usable
21.6 GB (90% of its memory)
Generation speed
179–269 tok/s (reads the active 3.3B per token)
Longest context
37K (longer doesn’t fit)
First token
about 0.11–0.16 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file56.9 GB≈ 60.5Needs system memory 12–17 tok/s—
Q8_0maker’s file30.3 GB≈ 32.5Needs system memory 30–44 tok/s—
FP8maker’s file30.2 GB≈ 32.5Needs system memory 30–45 tok/s—
Q6_Kmaker’s file23.4 GB≈ 25.3Needs system memory 69–103 tok/s—
Q5_K_Mmaker’s file20.2 GB≈ 22.0Needs system memory 177–265 tok/s—
Q5_0maker’s file19.6 GB≈ 21.4Runs well 163–245 tok/s10K
Q4_K_Mmaker’s file17.3 GB≈ 18.9Runs well 179–269 tok/s37K
Q4_016.0 GB≈ 17.5Runs well 189–284 tok/s128K
INT4 (AWQ / GPTQ)maker’s file15.8 GB≈ 17.3Runs well 191–287 tok/s128K
IQ4_XS15.1 GB≈ 16.6Runs well 197–295 tok/s128K
Q3_K_Mquality drops13.9 GB≈ 15.3Runs well 208–313 tok/s128K
IQ3_XXSquality drops10.9 GB≈ 12.2Runs well 244–366 tok/s128K
Q2_Kquality drops10.7 GB≈ 11.9Runs well 247–370 tok/s128K
IQ2_XXSquality drops7.3 GB≈ 8.6Runs well 305–457 tok/s128K

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 M6 (24GB or 32GB) · 32 GB.

Run commands

llama.cpp
llama-server -hf Qwen/Qwen3-30B-A3B-GGUF:Q4_K_M -c 8192
Ollama
ollama run hf.co/Qwen/Qwen3-30B-A3B-GGUF:Q4_K_M
Memory to fine-tune
Full≈ 457.1 GBDoesn’t fit your hardware
LoRA≈ 59.2 GBDoesn’t fit your hardware
QLoRA≈ 18.4 GBFits 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 134.3

    #109 of 118 listed models · CI 131.4–136.9

  • LMArena Text 1,327

    #136 of 163 listed models · CI 1,322–1,331 · 26,156 votes

  • Epoch AI · GPQA Diamond 61.7%

    #109 of 168 listed models · ±2.97

    Also listed: reasoning off 50.4%

  • Epoch AI · OTIS Mock AIME 2024–2025 62.8%

    #100 of 159 listed models · ±6.21

    Also listed: reasoning off 25.6%

  • Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Nov 4 – 5, 2026
  • LMArena (Arena), leaderboard-dataset on Hugging Face, 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. Now priced on Cloudflare Workers AI Input $0.051 · Output $0.335
  2. Context window 256K → 131.07K
  3. Max output 8.19K → 16.38K

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