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Qwen1.5 7B Chat

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Qwen1.5 7B Chat is an open-weights language model from Alibaba, released February 2024. Its published size is 7.72B parameters and a 32k-token context window. Licence: tongyi-qianwen.

Catalogue identity notes

Arena entry 'qwen1.5-7b-chat' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'qwen1.5-7b-chat' as 'Qwen1.5-7B-Chat' (Alibaba, Qianwen LICENSE), linking https://qwenlm.github.io/blog/qwen1.5/. Maker repo Qwen/Qwen1.5-7B-Chat @5f4f5e6: licence tag other (tongyi-qianwen). 7,721,324,544 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-02: Qwen blog qwenlm.github.io/blog/qwen1.5/ (datePublished 2024-02-04).

36.6±0.8#582 of 719 LLMsfrom 13 benchmark results across 10 sources

$ how it compares

The LLMs ranked either side of Qwen1.5 7B Chat, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
673Dolphin 2.2.1 Mistral 7B Cognitive Computations37±2local~6.1GB2023-10
674Code Llama 13B (base) Meta37±1local~9.2GB2023-08
675Qwen1.5 7B Chat Alibaba37±1local~6.5GB2024-02
676Yi 1.5 6B Chat 01.AI37±2local~5.1GB2024-05
677Falcon3 3B Instruct TII37±3local~3.4GB2024-12
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #394 of 458 * · 26% of its score · 1 test

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Falcon3 3B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval42.1% ✎▁█▄▅▇█·▄▇▅▄▄ beats 11/42easy · avg 78%▇▇▇▇▇▇▇▇▇▇ 26%sharp · at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #352 of 393 · 21% of its score · 3 tests

Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-3-mini-4k-instruct (June 2024 update) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 H 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled14.8% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 39/122medium · avg 39%▇▇▇▇ 10%typical
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1086.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 40/95avg 1201▇▇ 5.3%blunt
LMArena (text)1143.8 ✎▁▁▁▃▃▃▃▆▆▇█▃ beats 36/379avg 1268▇▇ 5.2%blunt
other tests9 not in a task · 53% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-2618.0% ✎▁▁▁▂▂▃▂█▆▃▅▃ beats 12/61medium · avg 41%▇▇▇▇▇ 14%typical
EvoEval (overall)25.5% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 10/42medium · avg 55%▇▇▇▇▇ 13%at its level
MMLU-Pro26.4% ✎▃▂▅█▃·█▃▅··▂ beats 1/23medium · avg 57%▇▇▇ 6.9%typical
WildBench v2 WB-Score (task macro)23.4232 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 6/52avg 45.199 WB-Score▇▇ 5.9%sharp
MedQA (USMLE, 4 options)45.4% ✎▁▁··▄▁▁··▄▃█ beats 8/17easy · avg 76%▇▇ 5.8%sharp · at its level
MedMCQA45.3% ✎▂▂··▄▄▂▂·▄██ beats 9/17medium · avg 68%▇ 3.2%at its level
Polish EQ-Bench v2 (SpeakLeash)25.0% ✎▁··▁▁▃▂▆▅▆█▆ beats 4/70medium · avg 64%▇ 2.8%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)67.7% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 138/276medium · avg 73%▇ 1.0%at its level · noisy
1 more test
testresultvs the others in its rundifficultycountswhy
PubMedQA58.6% ✎▂·▂▆·▄▂▄▄▂█▂ beats 2/17medium · avg 77%▇ 0.4%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 Qwen1.5 7B Chat · needs ~4.8 GB · from huggingface.co/Qwen (official)
    chat in the terminal
    llama cli -hf Qwen/Qwen1.5-7B-Chat-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/Qwen1.5-7B-Chat-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 Qwen1.5 7B Chat · needs ~4.8 GB · from huggingface.co/Qwen (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/Qwen1.5-7B-Chat-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 Qwen1.5 7B Chat · needs ~5.13 GB · from ollama.com
    chat in the terminal
    ollama run qwen:7b-chat-v1.5-q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Qwen1.5 7B Chat · needs ~5.2 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen1.5-7B-Chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen1.5-7B-Chat-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Top Leaderboard Ranking = Top… 39%, LiveBench model judgments (HF… 14%, AlpacaEval 1.0 and 2.0 leader… 10% and 7 more

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
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇▇▇▇▇ 39%CC BY 4.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇ 14%Apache-2.0 (stated in the LiveBench datasheet)
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇ 10%Apache-2.0
Open Medical-LLM Leaderboard results (HF dataset openlifescienceai/results)3▇▇▇ 9%Apache-2.0
MMLU-Pro author-released evaluation runs1▇▇ 7%Apache-2.0
WildBench v2 leaderboard data (Ai2)1▇▇ 6%Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇ 5%CC-BY-4.0
LMArena leaderboard dataset1▇▇ 5%CC BY 4.0
SpeakLeash Polish EQ-Bench leaderboard1▇ 3%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 36.6

$ improve this page

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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