[whatmodel.sh](../../README.md) / OpenThinker3 7B
# OpenThinker3 7B
Open Thoughts · released 2025-05 · 7.62B params · apache-2.0
**score 50.5** ±0.9 · #359 of 732 · from 17 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenThinker3 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #261 of 469 10% 3
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #345 of 402* 5.3% 2
math ▁▁▃▅█▆●█▄▂▁▁ #170 of 431 36% 4
coding · #261 of 469 · 10% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval+ 87.4% ▂·······▂·●█ beats 2/6 easy · avg 78% 3.5% sharp · noisy
MBPP+ 61.4% ▄●····▄·▄··█ beats 1/5 medium · avg 61% 3.3% at its level
LiveCodeBench v3 68.0% ▃▃·····●·▃·█ beats 2/6 medium · avg 64% 3.2% at its level
chat & writing · #345 of 402 · 5.3% of its score
Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 24.0% ●···█·█·█·██ beats 0/5 medium · avg 43% 4.7% sharp · at its level
IFEval (aggregation unspecified) 51.7% ●▄·····█▄▄·▄ beats 0/6 easy · avg 79% 0.6% noisy
math · #170 of 431 · 36% of its score
Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M2.7](minimax-m2-7.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
AIME 2025 57.2% ▄··▄····●▄▄▄ beats 2/6 medium · avg 50% 13% sharp · at its level
AIME 2024 67.7% ▄·····▄··●██ beats 2/6 medium · avg 58% 11% sharp · at its level
MATH (subset unspecified) 94.5% ▃·····▃···●█ beats 3/6 very easy · avg 91% 8.2% typical
OMEGA (math generalisation) 38.4% ▄······█●▄·█ beats 3/6 hard · avg 31% 5.1% at its level
other tests (8)
test result vs the others in its run difficulty counts why
BIG-Bench Hard 77.1% ▄·····▄·●·██ beats 2/6 easy · avg 77% 12% sharp · at its level
GPQA (subset unspecified) 47.6% ▃······▃·●·█ beats 3/6 hard · avg 43% 10% sharp · at its level
MMLU 77.4% ▄···▄··●··▄█ beats 2/6 easy · avg 76% 8.4% at its level
AGIEval English 78.6% ▄····●·▄▄··█ beats 1/5 easy · avg 76% 4.7% typical
BRIDGE clinical text, zero-shot a… 19.8% ▁▁▁▁●▂▂▃▄▆█▃ beats 8/97 hard · avg 31% 4.6% blunt · at its level
ZebraLogic 34.9% █●····▄·▄·▄▄ beats 2/6 medium · avg 52% 3.6% at its level
PopQA 18.0% ▄··●···▄···█ beats 2/5 hard · avg 20% 2.5% typical
IFBench (aggregation unspecified) 23.0% ●·██·██····█ beats 0/5 medium · avg 36% 1.8% at its level
source results share of the score
Olmo 3 14 82% ▇▇▇▇▇▇▇▇▇▇
allenai/Olmo-3-7B-Think model card 2 13% ▇▇
BRIDGE Medical Leaderboard 1 5% ▇
prior: pull toward the middle 0% ▇
weighted average = 50.5
Licences: Olmo 3 ([CC BY 4.0](../../licenses/report-2512.13961v2.txt)) · allenai/Olmo-3-7B-Think model card ([Apache-2.0](../../licenses/allenai--Olmo-3-7B-Think.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.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 OpenThinker3 7B (Q4_K_M, needs ~4.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/OpenThinker3-7B-GGUF/tree/731c10552be5259f925472894a613c0c5bc9cc28))
```sh
llama cli -hf lmstudio-community/OpenThinker3-7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/OpenThinker3-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.1 GB) `llama cli -hf lmstudio-community/OpenThinker3-7B-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 OpenThinker3 7B (Q4_K_M, needs ~4.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/OpenThinker3-7B-GGUF/tree/731c10552be5259f925472894a613c0c5bc9cc28))
```sh
lms get https://huggingface.co/lmstudio-community/OpenThinker3-7B-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 (~8.1 GB) `lms get https://huggingface.co/lmstudio-community/OpenThinker3-7B-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/OpenThinker3-7B-4bit -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/OpenThinker3-7B-8bit -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 OpenThinker3 7B (Q4_K_M, needs ~4.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/OpenThinker3-7B-GGUF/tree/731c10552be5259f925472894a613c0c5bc9cc28))
```sh
ollama run hf.co/lmstudio-community/OpenThinker3-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.1 GB) `ollama run hf.co/lmstudio-community/OpenThinker3-7B-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run OpenThinker3 7B (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/OpenThinker3-7B-4bit/tree/020527702903f2dd0cf3d53d18b10a2a52b2562d))
```sh
mlx_lm.chat --model mlx-community/OpenThinker3-7B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/OpenThinker3-7B-4bit
```
Other versions: 8bit (~8.1 GB, Mac only) `mlx_lm.chat --model mlx-community/OpenThinker3-7B-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~18.79 GB, NVIDIA only, from [huggingface.co/open-thoughts](https://huggingface.co/open-thoughts/OpenThinker3-7B))
```sh
vllm serve open-thoughts/OpenThinker3-7B
```
Model repository and documentation: [huggingface.co/open-thoughts](https://huggingface.co/open-thoughts/OpenThinker3-7B)
[interactive version](https://whatmodel.sh/model/openthinker3-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)