[whatmodel.sh](../../README.md) / OpenChat 3.5 0106
# OpenChat 3.5 0106
OpenChat · released 2024-01 · 7.24B params · apache-2.0
**score 47.2** ±0.6 · #428 of 732 · from 10 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenChat 3.5 0106 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #304 of 469 73% 3
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #324 of 402* 8.1% 2
coding · #304 of 469 · 73% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 72.6% ▁·▁▁▃▄▆▅●▃▁▁ beats 64/99 easy · avg 76% 44% sharp · at its level · several runs
HumanEval+ 67.7% ▁·▁▁▁▃▄▄▄●▃▁ beats 66/99 easy · avg 70% 24% sharp · at its level
MBPP+ 54.5% ▁···▁▁▂▅●█▆▂ beats 26/75 easy · avg 66% 4.7% at its level
chat & writing · #324 of 402 · 8.1% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1182.8 ▁▁▁▂●▃▃▆▆▇█▃ beats 58/385 avg 1260 4.2% blunt
Polyrating rating (bias-aware re-… 1114.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 55/95 avg 1187 3.9% typical
other tests (4)
test result vs the others in its run difficulty counts why
EvoEval (overall) 42.1% ▂▂▃▄▄█▁●▅▃▁▃ beats 29/42 medium · avg 52% 12% at its level
CPTUB average (SpeakLeash, 0-5 ju… 52.6% ▁▂▂▂●█▃▆▅▄▂▃ beats 17/95 medium · avg 65% 3.3% at its level
Polish EQ-Bench v2 (SpeakLeash) 43.8% ▁··▁▁▂▂●▅▆█▆ beats 13/70 medium · avg 62% 2.6% at its level
Open PT-LLM Leaderboard average (… 70.5% ▃▁▁▁▁▁▁▁▂●▅▁ beats 168/276 medium · avg 72% 1.0% at its level · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 56% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 29% ▇▇▇
LMArena leaderboard dataset 1 4% ▇
Polyrating: A Cost-Effective and Bias-A… 1 4% ▇
SpeakLeash CPTUB 1 3% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 47.2
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run OpenChat 3.5 0106 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF/tree/a0151450e53700eb4af146830c3ba394035597d6))
```sh
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)
```sh
llama serve -hf TheBloke/openchat-3.5-0106-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf TheBloke/openchat-3.5-0106-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run OpenChat 3.5 0106 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF/tree/a0151450e53700eb4af146830c3ba394035597d6))
```sh
lms get https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.7 GB) `lms get https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run OpenChat 3.5 0106 (Q4_K_M, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/openchat/tags))
```sh
ollama run openchat:7b-v3.5-0106-q4_K_M
```
Other versions: Q8_0 (~9.19 GB) `ollama run openchat:7b-v3.5-0106-q8_0`
Model repository and documentation: [huggingface.co/openchat](https://huggingface.co/openchat/openchat-3.5-0106)
[interactive version](https://whatmodel.sh/model/openchat-3-5-0106) · [JSON](https://whatmodel.sh/api?format=json&all=1)