[whatmodel.sh](../../README.md) / OpenThinker2 7B # OpenThinker2 7B Open Thoughts · released 2025-04 · 7.62B params · apache-2.0 **score 49.0** ±1.7 · #391 of 732 · from 5 benchmark results across 1 source · † 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 OpenThinker2 7B among every model on that task or test, worst to best.}

task    vs the others          rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #299 of 469*    6.5%      1
math    ▁▁▃▅█●▄█▄▂▁▁    #196 of 431     58%      3

coding · #299 of 469 · 6.5% 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
LiveCodeBench v2 (maker)   55.6%  ▄··█·●···▄▄█ beats 3/7    medium · avg 53%    6.5%  at its level · small run

math · #196 of 431 · 58% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank lower overall.

test               result  vs the others in its run   difficulty           counts  why
AIME 2025 (maker)   33.3%  ▁·▁▁▁▂●▆█▇▅▁ beats 21/133  medium · avg 38%        28%  sharp · at its level · small run
AIME 2024 (maker)   50.0%  ▁▁▁▂▁▃▁●███▃ beats 22/85   medium · avg 54%        18%  sharp · at its level · small run
MATH-500 (maker)    88.4%  ▁▃▂▃▄●▅▇█▄▂▁ beats 35/85   very easy · avg 89%     11%  small run

other tests (1)

test                  result  vs the others in its run   difficulty        counts  why
GPQA Diamond (maker)   49.3%  ▁▁▁▁▂▄●▅███▄ beats 85/322  medium · avg 52%     35%  sharp · at its level · small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
open-thoughts/OpenThinker2-7B model car…        5   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 49.0

_{Licences: open-thoughts/OpenThinker2-7B 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 OpenThinker2 7B (Q4_K_M, needs ~4.68 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF/tree/894134150cf4e8a58cebd62c27b8998c764bff33)) ```sh llama cli -hf bartowski/open-thoughts_OpenThinker2-7B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/open-thoughts_OpenThinker2-7B-GGUF:Q4_K_M ``` ### 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 OpenThinker2 7B (Q4_K_M, needs ~4.68 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF/tree/894134150cf4e8a58cebd62c27b8998c764bff33)) ```sh lms get https://huggingface.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run OpenThinker2 7B (Q4_K_M, needs ~4.68 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF/tree/894134150cf4e8a58cebd62c27b8998c764bff33)) ```sh ollama run hf.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/open-thoughts](https://huggingface.co/open-thoughts/OpenThinker2-7B) [interactive version](https://whatmodel.sh/model/openthinker2-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)