Qwen3-8B

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 qwen3-8b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.18$0.70
Reasoning off no longer listed since Oct 11, 2026 —$0.70
Reasoning on no longer listed since Oct 11, 2026 —$2.10

Prices from qwen3-8b 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 qwen3-8b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.072$0.287
Reasoning off no longer listed since Oct 11, 2026 —$0.287
Reasoning on no longer listed since Oct 11, 2026 —$0.717

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026) · qwen3-8b model (opens in a new tab) (checked Oct 5, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3-8b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥0.50—
Reasoning off no longer listed since Oct 11, 2026 —¥2.00
Reasoning on no longer listed since Oct 11, 2026 —¥5.00

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

Knowledge cutoff
Apr 2025
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, aliases.

Run locally

Weights: Qwen/Qwen3-8B
  • Parameters 8.2B
  • Dense
  • Native precision BF16
  • Layers 36
  • KV cache per token 144 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 BF16 (native) (automatic) 8K context 29–43 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 17.1 GB (weights 15.3 (maker’s file) + KV cache 1.1 + overhead 0.8)
Usable
21.6 GB (90% of its memory)
Generation speed
29–43 tok/s
Longest context
39K (longer doesn’t fit)
First token
about 0.26–0.40 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file15.3 GB≈ 17.1Runs well 29–43 tok/s39K
Q8_0maker’s file8.1 GB≈ 9.7Runs well 51–76 tok/s128K
FP8maker’s file8.8 GB≈ 10.4Runs well 47–71 tok/s128K
Q6_Kmaker’s file6.3 GB≈ 7.9Runs well 64–95 tok/s128K
Q5_K_Mmaker’s file5.4 GB≈ 7.1Runs well 71–107 tok/s128K
Q5_0maker’s file5.3 GB≈ 7.0Runs well 73–109 tok/s128K
Q4_K_Mmaker’s file4.7 GB≈ 6.3Runs well 81–121 tok/s128K
Q4_04.3 GB≈ 5.9Runs well 87–130 tok/s128K
INT4 (AWQ / GPTQ)maker’s file5.7 GB≈ 7.3Runs well 69–103 tok/s128K
IQ4_XS4.1 GB≈ 5.7Runs well 91–136 tok/s128K
Q3_K_Mquality drops3.7 GB≈ 5.4Runs well 97–145 tok/s128K
IQ3_XXSquality drops2.9 GB≈ 4.5Runs well 116–174 tok/s128K
Q2_Kquality drops2.9 GB≈ 4.5Runs well 118–177 tok/s128K
IQ2_XXSquality drops2.0 GB≈ 3.6Runs well 152–228 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 3080; unified memory Apple M5 · 16 GB.

Run commands

vLLM
vllm serve Qwen/Qwen3-8B --max-model-len 8192
Memory to fine-tune
Full≈ 124.5 GBDoesn’t fit your hardware
LoRA≈ 18.0 GBFits your hardware
QLoRA≈ 7.0 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 132.2

    #112 of 118 listed models · CI 129.1–135.1

  • Epoch AI · GPQA Diamond 56.8%

    #113 of 168 listed models · ±2.93

    Also listed: reasoning off 47.5%

  • Epoch AI · OTIS Mock AIME 2024–2025 56.1%

    #106 of 159 listed models · ±6.45

    Also listed: reasoning off 22.2%

  • 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
  • changes we made
  1. Context window 256K → 131.07K

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