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Qwen1.5 110B Chat

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Qwen1.5 110B Chat is an open-weights language model from Alibaba, released April 2024. Its published size is 111B parameters and a 32k-token context window. Licence: tongyi-qianwen.

Catalogue identity notes

Arena entry 'qwen1.5-110b-chat' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'qwen1.5-110b-chat' as 'Qwen1.5-110B-Chat' (Alibaba, Qianwen LICENSE), linking https://qwenlm.github.io/blog/qwen1.5-110b/. Maker repo Qwen/Qwen1.5-110B-Chat @85f86ce: licence tag other (tongyi-qianwen). 111,209,914,368 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: Qwen blog qwenlm.github.io/blog/qwen1.5-110b/ (datePublished 2024-04-25).

47.0±0.9#398 of 719 LLMsfrom 8 benchmark results across 7 sources

$ how it compares

The LLMs ranked either side of Qwen1.5 110B Chat, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
473Yi-1.5 34B (base) 01.AI47±3local~22GB2024-05
474Phi-4-mini-reasoning 3.8B Microsoft47±2api $0.13/M +local~3.8GB2025-04
475Qwen1.5 110B Chat Alibaba47±1local~45GB2024-04
476WhiteRabbitNeo-33B-v1 WhiteRabbitNeo47±1local~22GB2023-12
477DeepSeek R1 Distill Llama 8B DeepSeek47±1local~6.2GB2025-01
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▁▁▁▃▆██▇▆▄▃▁ #333 of 458 * · 13% of its score · 1 test

Beats GLM-4.7-Flash, LFM2.5 2.6B, LFM2.5 8B-A1B here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), DeepSeek LLM 67B Chat, WizardLM 70B V1.0 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Aider code editing (133 Exercism Python exercises, two attempts)37.6% ✎▁·▁▂▄▁▆▄█▆▂▂ beats 6/62medium · avg 54%▇▇▇▇▇ 13%at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #278 of 393 · 40% of its score · 3 tests

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, Ministral 3 8B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled43.9% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 100/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇ 26%at its level
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1161.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 71/95avg 1201▇▇▇ 7.0%blunt
LMArena (text)1234.1 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 86/379avg 1268▇▇▇ 6.9%blunt
other tests4 not in a task · 46% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-2629.8% ✎▁▁▁▂▂▃▂█▆▃▅▃ beats 27/61medium · avg 41%▇▇▇▇▇▇▇▇▇▇▇ 28%at its level
BigCodeBench (Instruct) lab33.6% ✎▃▂·▆▆██▆▅▃▃▃ beats 13/33medium · avg 41%▇▇▇ 8.5%at its level
BigCodeBench (Complete) lab43.2% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 34/58medium · avg 50%▇▇▇ 8.3%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)74.7% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 220/276medium · avg 73%▇ 1.5%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 110B Chat · needs ~67.2 GB · from huggingface.co/second-state
    chat in the terminal
    llama cli -hf second-state/Qwen1.5-110B-Chat-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf second-state/Qwen1.5-110B-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 110B Chat · needs ~67.2 GB · from huggingface.co/second-state

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/second-state/Qwen1.5-110B-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 110B Chat · needs ~67.1 GB · from ollama.com
    chat in the terminal
    ollama run qwen:110b-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 110B Chat · needs ~62.6 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen1.5-110B-Chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen1.5-110B-Chat-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · LiveBench model judgments (HF… 28%, AlpacaEval 1.0 and 2.0 leader… 26%, BigCodeBench paper (Zhuo et a… 17% and 4 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
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇ 28%Apache-2.0 (stated in the LiveBench datasheet)
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇▇▇▇ 26%Apache-2.0
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v4 · lab-reported2▇▇▇▇▇ 17%CC BY 4.0
Aider code editing benchmark runs (old edit leaderboard)1▇▇▇▇ 13%Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇ 7%CC-BY-4.0
LMArena leaderboard dataset1▇▇ 7%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 2%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 47.0

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