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OpenChat 3.5 0106

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OpenChat 3.5 0106 is an open-weights language model from OpenChat, released January 2024. Its published size is 7.24B parameters and a 8k-token context window. Licence: apache-2.0.

Catalogue identity notes

Arena entry 'openchat-3.5-0106' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'openchat-3.5-0106' as 'OpenChat-3.5-0106' (OpenChat, Apache-2.0), linking https://huggingface.co/openchat/openchat-3.5-0106. Maker repo openchat/openchat-3.5-0106 @ff058fd: licence tag apache-2.0. 7,241,748,480 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-07).

45.9±0.6#424 of 719 LLMsfrom 10 benchmark results across 7 sources

$ how it compares

The LLMs ranked either side of OpenChat 3.5 0106, out of 828 with a score, then more from OpenChat.

#modelscorewhereRAMreleased
503Nova Micro Amazon46±2api $0.061/M2024-12
504T5 Base Google46±9local~2.4GB2019-10
505OpenChat 3.5 0106 OpenChat46±1local~6.1GB2024-01
506Qwen2-VL 7B Instruct Alibaba46±1local~6.4GB2024-08
507Qwen2.5-Math 1.5B Instruct Alibaba46±2local~2.4GB2024-09
more from OpenChat
518OpenChat 3.5 0106 Gemma OpenChat46±3local~6.9GB2024-03
603OpenChat 3.5 OpenChat41±2local~6.1GB2023-10

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #296 of 458 · 75% of its score · 3 tests

Beats Solar Open 100B (102B-A12B), GLM-4.7-Flash, Qwen3-VL 30B-A3B Instruct here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Yi 1.5 9B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +172.6% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 64/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 47%sharp · at its level · several runs
HumanEval+67.7% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 66/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇ 24%sharp · at its level
MBPP+54.5% ✎▁···▁▁▂▅▇█▆▂ beats 26/75easy · avg 68%▇▇ 4.4%at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #316 of 393 * · 7.3% of its score · 2 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
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1114.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 55/95avg 1201▇ 3.7%blunt
LMArena (text)1182.7 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 58/379avg 1268▇ 3.6%blunt
other tests4 not in a task · 17% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)42.1% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 29/42medium · avg 55%▇▇▇▇▇ 11%at its level
CPTUB average (SpeakLeash, 0-5 judge scale)52.6% ✎▁▂▂▂▃█▃▆▅▄▂▃ beats 17/95easy · avg 66%▇ 2.7%at its level
Polish EQ-Bench v2 (SpeakLeash)43.8% ✎▁··▁▁▂▂▆▅▆█▆ beats 13/70medium · avg 64%▇ 2.2%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)70.5% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 168/276medium · avg 73%▇ 0.8%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 OpenChat 3.5 0106 · needs ~4.4 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/openchat-3.5-0106-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/openchat-3.5-0106-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 OpenChat 3.5 0106 · needs ~4.4 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/openchat-3.5-0106-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 OpenChat 3.5 0106 · needs ~4.84 GB · from ollama.com
    chat in the terminal
    ollama run openchat:7b-v3.5-0106-q4_K_M

Official weights and documentation: huggingface.co/openchat ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 59%, Top Leaderboard Ranking = Top… 28%, Polyrating: A Cost-Effective… 4% 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
EvalPlus / EvalPerf published evaluation runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%Apache-2.0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇ 28%CC BY 4.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇ 4%CC-BY-4.0
LMArena leaderboard dataset1▇ 4%CC BY 4.0
SpeakLeash CPTUB (Complex Polish Text Understanding Benchmark)1▇ 3%MIT
SpeakLeash Polish EQ-Bench leaderboard1▇ 2%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 45.9

$ 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