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CodeQwen1.5 7B Chat

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CodeQwen1.5 7B Chat is an open-weights language model from Alibaba, released April 2024. Its published size is 7.25B parameters and a 64k-token context window. Licence: tongyi-qianwen.

Catalogue identity notes

Maker repo Qwen/CodeQwen1.5-7B-Chat @7b0cc33: licence tag other. 7,250,284,544 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-15). Chat checkpoint of CodeQwen1.5 (April 2024), distinct from the CodeQwen1.5-7B base and from Qwen2.5-Coder. Ollama tags codeqwen:7b-chat-v1.5-q4_K_M / -q8_0 are the library's chat builds; sizes estimated from the parameter count. Evaluated in this lane's sources as 'Qwen--CodeQwen1.5-7B-Chat'.

50.4±0.7#336 of 719 LLMsfrom 14 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of CodeQwen1.5 7B Chat, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
403Bielik 11B v2.0 Instruct SpeakLeash51±3local~8.5GB2024-08
404OpenThinker3 7B Open Thoughts50±1local~6GB2025-05
405CodeQwen1.5 7B Chat Alibaba50±1local~6.4GB2024-04
406Jamba 1.5 Large AI21 Labs50±1local~839GB2024-08
407Gemini 1.5 Flash (001) Google50±1comparison only2024-05
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▁▁▁▃▆██▇▆▄▃▁ #241 of 458 · 83% of its score · 7 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails QwQ-32B-Preview, InternLM2.5 20B Chat, Mistral Small Instruct 22B (2409) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +183.5% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 89/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 36%sharp · several runs
HumanEval+ +178.7% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 89/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇ 31%sharp · at its level · several runs
MBPP+ +169.0% ✎▁···▁▁▂▅▇█▆▂ beats 61/75easy · avg 68%▇▇▇ 7.8%at its level · several runs
MBPP lab77.7% ✎█···███·██·█ beats 3/6easy · avg 78%▇ 3.4%typical
MultiPL-E lab70.8% ✎▄·▄····▄▄·▄█ beats 2/6easy · avg 71%▇ 2.9%at its level
CRUXEval-I lab44.0% ✎█·██·█··█·██ beats 1/6medium · avg 57%▇ 1.4%at its level
CRUXEval-O lab38.8% ✎▄·█··▄·▄··▄▄ beats 2/6medium · avg 55%▇ 0.5%blunt · at its level
other tests3 not in a task · 17% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Instruct) lab +139.3% ✎▃▂·▆▆██▆▅▃▃▃ beats 24/33medium · avg 41%▇▇▇ 7.9%at its level · several runs
BigCodeBench (Complete) lab43.7% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 39/58medium · avg 50%▇▇ 5.9%at its level
EvalPerf correctness (118 tasks, 20240328)74.1% ✎▂·▂▂▂▆▅▆▆▆▃█ beats 10/29easy · avg 76%▇ 2.9%typical

$ 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 CodeQwen1.5 7B Chat · needs ~4.7 GB · from huggingface.co/Qwen
    chat in the terminal
    llama cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/CodeQwen1.5-7B-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 CodeQwen1.5 7B Chat · needs ~4.7 GB · from huggingface.co/Qwen

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/CodeQwen1.5-7B-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 CodeQwen1.5 7B Chat · needs ~4.85 GB · from ollama.com
    chat in the terminal
    ollama run codeqwen: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 CodeQwen1.5 7B Chat · needs ~5.9 GB, Mac only · from huggingface.co/Wwayu
    chat in the terminal
    mlx_lm.chat --model Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit

Weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 64%, Qwen (Alibaba) vendor-reporte… 24%, BigCodeBench paper (Zhuo et a… 12%

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
EvalPlus / EvalPerf published evaluation runs4▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%Apache-2.0
Qwen (Alibaba) vendor-reported benchmark scores for its own models (qwen.ai launch posts, Qwen2.5-VL and Qwen3-VL technical reports, Qwen3.8-Flash-Next card) · lab-reported8▇▇▇▇▇▇▇ 24%Factual vendor-reported score (not licensed content)
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v4 · lab-reported2▇▇▇▇ 12%CC BY 4.0
prior · pull toward the middle▇ 0%
weighted average= 50.4

$ improve this page

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