[whatmodel.sh](../../README.md) / OpenThinker 7B
# OpenThinker 7B
Open Thoughts · released 2025-01 · 7.62B params · apache-2.0
**score 45.1** ±1.6 · #477 of 732 · from 9 benchmark results across 2 sources · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenThinker 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #434 of 469* 11% 1
math ▁▁▃▅█●▄█▄▂▁▁ #241 of 431 55% 3
coding · #434 of 469 · 11% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [Mistral 7B Instruct v0.1](mistral-7b-instruct-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v2 (maker) 39.9% ●··█·▄···▄▄█ beats 0/7 medium · avg 50% 11% at its level · several runs
math · #241 of 431 · 55% of its score
Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 23.3% ▁·▁▁▁●▅▆█▇▅▁ beats 13/133 medium · avg 38% 22% sharp · small run
AIME 2024 (maker) 31.3% ▁▁▁▂▁▃●▅███▃ beats 17/85 medium · avg 54% 18% sharp · at its level · small run · seve…
MATH-500 (maker) 83.0% ▁▃▂▃▄●▅▇█▄▂▁ beats 25/85 very easy · avg 89% 15% small run · several runs
other tests (1)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 42.4% ▁▁▁▁▂▄●▅███▄ beats 60/322 medium · avg 52% 32% sharp · small run · several runs
source results share of the score
open-thoughts/OpenThinker2-7B model car… 5 81% ▇▇▇▇▇▇▇▇▇▇
open-thoughts/OpenThinker-7B model card… 4 18% ▇▇
prior: pull toward the middle 1% ▇
weighted average = 45.1
Licences: open-thoughts/OpenThinker2-7B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · open-thoughts/OpenThinker-7B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 OpenThinker 7B (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/OpenThinker-7B-GGUF/tree/091cdd601d3dc6c2c55923a965e41eb199d912ec))
```sh
llama cli -hf bartowski/OpenThinker-7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/OpenThinker-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.1 GB) `llama cli -hf bartowski/OpenThinker-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 OpenThinker 7B (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/OpenThinker-7B-GGUF/tree/091cdd601d3dc6c2c55923a965e41eb199d912ec))
```sh
lms get https://huggingface.co/bartowski/OpenThinker-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/bartowski/OpenThinker-7B-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-4bit-mlx -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx -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 OpenThinker 7B (Q4_K_M, needs ~4.7 GB, from [ollama.com](https://ollama.com/library/openthinker/tags))
```sh
ollama run openthinker:7b
```
### 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 OpenThinker 7B (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/AlejandroOlmedo](https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-4bit-mlx/tree/2a9a5be5af1e2aeceec0db21fe9cdcd065f4dd9d))
```sh
mlx_lm.chat --model AlejandroOlmedo/OpenThinker-7B-4bit-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model AlejandroOlmedo/OpenThinker-7B-4bit-mlx
```
Other versions: 8bit (~8.1 GB, Mac only) `mlx_lm.chat --model AlejandroOlmedo/OpenThinker-7B-8bit-mlx`
Model repository and documentation: [huggingface.co/open-thoughts](https://huggingface.co/open-thoughts/OpenThinker-7B)
[interactive version](https://whatmodel.sh/model/openthinker-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)