whatmodel.sh / QwenLong-L1-32B

QwenLong-L1-32B

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QwenLong-L1-32B is an open-weights language model from Alibaba (Tongyi Zhiwen), released May 2025. Its published size is 32.76B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo Tongyi-Zhiwen/QwenLong-L1-32B @a232c10: licence tag apache-2.0. 32,763,876,352 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-05: month the maker's Hugging Face repo was created (createdAt 2025-05-23).

57.0±4.4#279 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of QwenLong-L1-32B, out of 1132 with a score.

#modelscorewhereRAMreleased
362GPT-4o (2024-05-13) OpenAI57±0api $7.50/M2024-05
363QED-Nano LM Provers57±2local~4.1GB2026-02
364QwenLong-L1-32B Alibaba (Tongyi Zhiwen)57±4local~21GB2025-05
365Llama 4 Maverick (402B-A17B) Meta57±1api $0.42/M +local~65GB2025-04
366Claude 3.5 Sonnet (2024-06-20) Anthropic57±1comparison only2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 92% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)39.3% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 66/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%blunt · at its level

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run QwenLong-L1-32B · needs ~19.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/QwenLong-L1-32B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/QwenLong-L1-32B-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run QwenLong-L1-32B · needs ~19.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/QwenLong-L1-32B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run QwenLong-L1-32B · needs ~19.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/QwenLong-L1-32B-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run QwenLong-L1-32B · needs ~18.4 GB, Mac only · from huggingface.co/cnfusion
    chat in the terminal
    mlx_lm.chat --model cnfusion/QwenLong-L1-32B-mlx-4Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model cnfusion/QwenLong-L1-32B-mlx-4Bit

Other ways to run it

  1. Run it as published · needs ~65.53 GB · from huggingface.co/Tongyi-Zhiwen (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Tongyi-Zhiwen/QwenLong-L1-32B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Tongyi-Zhiwen ↗

$ how the score was worked out · BRIDGE Medical Leaderboard (H… 92%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%MIT
prior · pull toward the middle▇▇▇ 8%
weighted average= 57.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json