Qwen3-30B-A3B-Thinking-2507

Language Alibaba Qwen Verified Released Jul 30, 2025
  • In Input modalities: Text Out Output modalities: Text
  • Reasoning
  • Tool use
  • Open weights

Prices

3 offerings · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official Global · USD qwen3-30b-a3b-thinking-2507
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.108$1.076

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official International · USD qwen3-30b-a3b-thinking-2507
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.20$2.40

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3-30b-a3b-thinking-2507
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥0.75¥7.50

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Oct 8, 2026)

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

Details

Structured output
—
Family
qwen-thinking
Model ID

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

  • Parameters 30.5B
  • Active 3.3B · 128 experts, 8 per token
  • Native precision BF16
  • Layers 48
  • KV cache per token 96 KB
  • Native context 256K (model card)
  • Config positions 256K (config.json max_position_embeddings)
  • API context 80K (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 + 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_030.2 GB≈ 32.5Needs system memory 30–45 tok/s—
FP8maker’s file29.0 GB≈ 31.2Needs system memory 34–51 tok/s—
Q6_K23.4 GB≈ 25.3Needs system memory 69–103 tok/s—
Q5_K_M20.2 GB≈ 22.0Needs system memory 177–265 tok/s—
Q5_019.5 GB≈ 21.3Runs well 164–246 tok/s11K
Q4_K_M17.3 GB≈ 18.9Runs well 179–269 tok/s37K
Q4_016.0 GB≈ 17.5Runs well 189–284 tok/s51K
IQ4_XS15.1 GB≈ 16.6Runs well 197–295 tok/s61K
Q3_K_Mquality drops13.9 GB≈ 15.3Runs well 208–313 tok/s75K
IQ3_XXSquality drops10.9 GB≈ 12.2Runs well 244–366 tok/s256K
Q2_Kquality drops10.7 GB≈ 11.9Runs well 247–370 tok/s256K
IQ2_XXSquality drops7.3 GB≈ 8.6Runs well 305–457 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 M6 (24GB or 32GB) · 32 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≈ 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 AI · GPQA Diamond 70.1%

    #96 of 168 listed models · ±2.83

  • Epoch AI · OTIS Mock AIME 2024–2025 70.3%

    #91 of 159 listed models · ±6.04

  • 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, 2026
  • changes we made
  1. Now priced on Alibaba Cloud Model Studio, China Input ¥0.75 · Output ¥7.50
  2. Context window 256K → 81.92K
  3. Now priced on Alibaba Cloud Model Studio, International Input $0.20 · Output $2.40

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