whatmodel.sh / Qwen2.5 Coder 1.5B Instruct

Qwen2.5 Coder 1.5B Instruct

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Qwen2.5 Coder 1.5B Instruct is an open-weights language model from Alibaba, released September 2024. Its published size is 1.54B parameters and a 33k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo Qwen/Qwen2.5-Coder-1.5B-Instruct @2e1fd39: licence tag apache-2.0; card 'Number of Parameters: 1.54B'; 1,543,714,304 parameters (BF16) in the safetensors metadata; dense. Released 2024-09: repo createdAt 2024-09-18. Ollama tags (observed 2026-10-09): qwen2.5-coder:1.5b = 1.5b-instruct = 1.5b-instruct-q4_K_M (digest d7372fd82851, 986MB); 1.5b-instruct-q8_0 1.6GB. Sizes as listed by Ollama (rounded by the page). Same family as the catalogued qwen2-5-coder-7b/14b/32b. Why proposed: Lands in the 48-model aider-code-editing run (one result only; low weight). Popular small local coding model.

42.8±1.2#485 of 719 LLMsfrom 9 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Qwen2.5 Coder 1.5B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
573T5 Small Google43±9local~1.8GB2019-10
574MiniCPM-V 2.6 OpenBMB43±2local~6.4GB2024-08
575Qwen2.5 Coder 1.5B Instruct Alibaba43±1local~2.4GB2024-09
576LFM2 2.6B Liquid AI43±2local~3GB2025-09
577SmolLM3 3B Hugging Face43±1local~3.3GB2025-07
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▁▁▁▃▆██▇▆▄▃▁ #348 of 458 · 96% of its score · 8 tests

Beats LFM2.5 2.6B, LFM2.5 8B-A1B, Qwen3-VL 2B Thinking here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B, DeepSeek LLM 67B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval lab70.7% ✎▄·▄···█··█·▄ beats 1/6very easy · avg 86%▇▇▇▇▇▇▇▇▇▇ 25%sharp · at its level
Aider code editing (133 Exercism Python exercises, two attempts)31.6% ✎▁·▁▃▄▁▆▄█▆▂▂ beats 3/62medium · avg 54%▇▇▇▇▇▇▇▇ 21%at its level
HumanEval+ lab66.5% ✎▄·▄····▄▄·▄█ beats 1/6easy · avg 82%▇▇▇▇▇▇▇▇ 19%sharp · at its level
MBPP lab69.2% ✎█···███·██·█ beats 1/6easy · avg 78%▇▇▇▇ 11%at its level
MultiPL-E lab56.7% ✎▄·▄····▄▄·▄█ beats 1/6easy · avg 71%▇▇▇ 8.2%at its level
MBPP+ lab59.4% ✎▄····▄▄·▄·█▄ beats 1/6easy · avg 67%▇▇▇ 7.0%at its level
CRUXEval-I lab45.4% ✎█·██·█··█·██ beats 2/6medium · avg 57%▇ 3.5%at its level
CRUXEval-O lab37.5% ✎▄·█··▄·▄··▄▄ beats 1/6medium · avg 55%▇ 1.2%blunt · at its level
other tests1 not in a task · 3.8% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Instruct) lab32.5% ✎▄······▄▄█·█ beats 1/6medium · avg 38%▇▇ 3.8%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 Qwen2.5 Coder 1.5B Instruct · needs ~1.1 GB · from huggingface.co/Qwen (official)
    chat in the terminal
    llama cli -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-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 Qwen2.5 Coder 1.5B Instruct · needs ~1.1 GB · from huggingface.co/Qwen (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-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 Qwen2.5 Coder 1.5B Instruct · needs ~0.99 GB · from ollama.com
    chat in the terminal
    ollama run qwen2.5-coder:1.5b

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 Qwen2.5 Coder 1.5B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/lmstudio-community
    chat in the terminal
    mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-1.5B-Instruct-MLX-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model lmstudio-community/Qwen2.5-Coder-1.5B-Instruct-MLX-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Qwen (Alibaba) vendor-reporte… 78%, Aider code editing benchmark… 21%

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
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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 78%Factual vendor-reported score (not licensed content)
Aider code editing benchmark runs (old edit leaderboard)1▇▇▇▇▇▇ 21%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 42.8

$ 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