Qwen3-14B

Language Alibaba Qwen Partly verified Released Apr 28, 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 qwen3-14b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.35$1.40
Reasoning off no longer listed since Oct 11, 2026 —$1.40
Reasoning on no longer listed since Oct 11, 2026 —$4.20

Prices from qwen3-14b model (opens in a new tab) (checked Oct 10, 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 qwen3-14b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 $0.144$0.574
Reasoning off no longer listed since Oct 11, 2026 —$0.574
Reasoning on no longer listed since Oct 11, 2026 —$1.434

Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Oct 11, 2026) · qwen3-14b model (opens in a new tab) (checked Oct 10, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen3-14b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥1.00—
Reasoning off no longer listed since Oct 11, 2026 —¥4.00
Reasoning on no longer listed since Oct 11, 2026 —¥10.00

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

Built-in tools
Web search
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, aliases.

Run locally

Weights: Qwen/Qwen3-14B
  • Parameters 14.8B
  • Dense
  • Native precision BF16
  • Layers 40
  • KV cache per token 160 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 Q8_0 (automatic) 8K context 30–44 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 16.6 GB (weights 14.6 (maker’s file) + KV cache 1.3 + overhead 0.7)
Usable
21.6 GB (90% of its memory)
Generation speed
30–44 tok/s
Longest context
40K (longer doesn’t fit)
First token
about 0.48–0.72 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file27.5 GB≈ 30.1Needs system memory 4–6 tok/s—
Q8_0maker’s file14.6 GB≈ 16.6Runs well 30–44 tok/s40K
FP8maker’s file15.2 GB≈ 17.2Runs well 29–43 tok/s36K
Q6_Kmaker’s file11.3 GB≈ 13.1Runs well 37–56 tok/s128K
Q5_K_Mmaker’s file9.8 GB≈ 11.5Runs well 43–64 tok/s128K
Q5_0maker’s file9.6 GB≈ 11.3Runs well 43–65 tok/s128K
Q4_K_Mmaker’s file8.4 GB≈ 10.1Runs well 49–73 tok/s128K
Q4_07.7 GB≈ 9.5Runs well 52–78 tok/s128K
INT4 (AWQ / GPTQ)maker’s file9.3 GB≈ 11.0Runs well 45–67 tok/s128K
IQ4_XS7.3 GB≈ 9.1Runs well 55–82 tok/s128K
Q3_K_Mquality drops6.7 GB≈ 8.5Runs well 59–88 tok/s128K
IQ3_XXSquality drops5.3 GB≈ 7.0Runs well 72–108 tok/s128K
Q2_Kquality drops5.2 GB≈ 6.9Runs well 73–110 tok/s128K
IQ2_XXSquality drops3.5 GB≈ 5.3Runs well 98–147 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 5070; unified memory Apple M5 · 16 GB.

Run commands

llama.cpp
llama-server -hf Qwen/Qwen3-14B-GGUF:Q8_0 -c 8192
Ollama
ollama run hf.co/Qwen/Qwen3-14B-GGUF:Q8_0
Memory to fine-tune
Full≈ 222.8 GBDoesn’t fit your hardware
LoRA≈ 30.6 GBDoesn’t fit your hardware
QLoRA≈ 10.8 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 136.2

    #104 of 118 listed models · CI 133.5–138.8

  • Epoch AI · GPQA Diamond 63.8%

    #107 of 168 listed models · ±2.93

    Also listed: reasoning off 53.4%

  • Epoch AI · OTIS Mock AIME 2024–2025 66.4%

    #96 of 159 listed models · ±6.19

    Also listed: reasoning off 25.8%

  • EQ-Bench Longform Writing 35.9

    #83 of 92 listed models

  • 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
  • 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 → 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