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Qwen1.5 14B Chat

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Qwen1.5 14B Chat is an open-weights language model from Alibaba, released February 2024. Its published size is 14.2B parameters and a 32k-token context window. Licence: tongyi-qianwen.

Catalogue identity notes

Arena entry 'qwen1.5-14b-chat' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'qwen1.5-14b-chat' as 'Qwen1.5-14B-Chat' (Alibaba, Qianwen LICENSE), linking https://qwenlm.github.io/blog/qwen1.5/. Maker repo Qwen/Qwen1.5-14B-Chat @9492b22: licence tag other (tongyi-qianwen). 14,167,290,880 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).

41.1±0.8#513 of 719 LLMsfrom 11 benchmark results across 9 sources

$ how it compares

The LLMs ranked either side of Qwen1.5 14B Chat, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
603OpenChat 3.5 OpenChat41±2local~6.1GB2023-10
604InternVL2-4B Shanghai AI Laboratory (OpenGVLab)41±2local~10GB2024-07
605Qwen1.5 14B Chat Alibaba41±1local~11GB2024-02
606Code Llama 34B Python Meta41±1local~23GB2023-08
607speechless-coding-7b-16k-tora uukuguy41±1local~16GB2023-10
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▁▁▁▃▆██▇▆▄▃▁ #366 of 458 * · 32% of its score · 1 test

Beats LFM2.5 8B-A1B, Qwen3-VL 2B Thinking, DeepSeek R1 Distill Qwen 7B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Phi-3-mini-128k-instruct (Apr 2024) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval50.0% ✎▁█▄▅▇█·▄▇▅▄▄ beats 20/42easy · avg 78%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%sharp · at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #313 of 393 · 33% of its score · 4 tests

Beats Gemma 4 E4B, OpenThinker3 7B, Gemma 4 E2B here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled23.9% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 62/122medium · avg 39%▇▇▇▇▇▇▇ 17%typical
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1112.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 54/95avg 1201▇▇ 6.2%blunt
LMArena (text)1191.0 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 65/379avg 1268▇▇ 6.1%blunt
Arabic IFEval prompt-level (Inception)5.0% ✎▁·▁▁▃▁▄▄▆▄█▄ beats 3/38medium · avg 49%▇▇ 4.0%sharp
other tests6 not in a task · 34% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)33.8% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 22/42medium · avg 55%▇▇▇▇▇▇▇ 18%at its level
MMLU-Pro36.2% ✎▃▂▅█▃·█▃▅··▂ beats 3/23medium · avg 57%▇▇▇▇ 9.5%at its level
Polish EQ-Bench v2 (SpeakLeash)51.3% ✎▁··▁▁▂▂▆▅▆█▆ beats 26/70medium · avg 64%▇ 3.6%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)72.6% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 198/276medium · avg 73%▇ 1.3%at its level · noisy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy79.6% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 71/100medium · avg 68%▇ 1.0%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy82.8% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 82/146easy · avg 83%▇ 0.9%at its level · noisy

$ 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 14B Chat · needs ~9.2 GB · from huggingface.co/Qwen (official)
    chat in the terminal
    llama cli -hf Qwen/Qwen1.5-14B-Chat-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/Qwen1.5-14B-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 14B Chat · needs ~9.2 GB · from huggingface.co/Qwen (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/Qwen1.5-14B-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 14B Chat · needs ~9.02 GB · from ollama.com
    chat in the terminal
    ollama run qwen:14b-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 14B Chat · needs ~9.1 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen1.5-14B-Chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen1.5-14B-Chat-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Top Leaderboard Ranking = Top… 50%, AlpacaEval 1.0 and 2.0 leader… 17%, MMLU-Pro author-released eval… 9% and 6 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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%CC BY 4.0
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇ 17%Apache-2.0
MMLU-Pro author-released evaluation runs1▇▇▇ 9%Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇ 6%CC-BY-4.0
LMArena leaderboard dataset1▇▇ 6%CC BY 4.0
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)1▇ 4%Apache-2.0
SpeakLeash Polish EQ-Bench leaderboard1▇ 4%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard2▇ 2%MIT
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 41.1

$ 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