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OpenThinker2 7B

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OpenThinker2 7B is an open-weights language model from Open Thoughts, released April 2025. Its published size is 7.62B parameters and a 33k-token context window. Licence: apache-2.0.

Catalogue identity notes

open-thoughts/OpenThinker2-7B @57922a0 (created 2025-04-03, licence apache-2.0). Open Thoughts' second-generation 7B reasoning model (OpenThoughts2-1M data), sibling of the catalogued OpenThinker2-32B and OpenThinker3-7B. Rows from its own Evalchemy table with OpenThinker-7B and DeepSeek-R1-Distill-Qwen-7B. No ollama library tag.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 7,615,616,512 parameters (open-thoughts/OpenThinker2-7B @57922a0).

49.3±1.7#357 of 719 LLMsfrom 5 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of OpenThinker2 7B, out of 828 with a score, then more from Open Thoughts.

#modelscorewhereRAMreleased
426Qwen2-Math 72B Instruct Alibaba49±1local~32GB2024-08
427OLMo 2 32B Instruct (0325) Ai249±1local~21GB2025-03
428OpenThinker2 7B Open Thoughts49±2local~6.4GB2025-04
429Aya 23 35B Cohere For AI49±3local~22GB2024-05
430Granite 4.1 3B IBM49±1local~3.7GB2026-04
more from Open Thoughts
327OpenThinker 32B Open Thoughts55±1local~21GB2025-02
331OpenThinker2 32B Open Thoughts54±1local~21GB2025-04
404OpenThinker3 7B Open Thoughts50±1local~6GB2025-05

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #290 of 458 * · 7.5% of its score · 1 test

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash 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
LiveCodeBench v2 lab55.6% ✎▄··█·▄···▄▄█ beats 3/7medium · avg 52%▇▇▇ 7.5%at its level · small run
math▁▂▃▅▆▅▄█▄▁▁▁ #194 of 398 · 59% of its score · 3 tests

Beats MiniMax-M2.1, Qwen3 235B-A22B (Apr 2025, hybrid), DeepSeek V3 (Dec '24) here, though they rank higher overall. Trails Phi-4-mini-reasoning 3.8B, DeepSeek R1 Distill Qwen 7B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AIME 2025 lab33.3% ✎▁·▁▁▁▂▅▆█▇▅▁ beats 21/133medium · avg 38%▇▇▇▇▇▇▇▇▇▇▇ 27%sharp · at its level · small run
AIME 2024 lab50.0% ✎▁▁▁▂▁▃▁▅███▃ beats 22/85medium · avg 53%▇▇▇▇▇▇▇▇ 20%sharp · at its level · small run
MATH-500 lab88.4% ✎▁▃▂▃▅▇▅▇█▅▂▁ beats 35/84very easy · avg 89%▇▇▇▇▇ 13%small run
other tests1 not in a task · 32% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond lab49.3% ✎▁▁▁▁▂▄▅▅███▄ beats 84/310hard · avg 50%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%sharp · at its level · small run

$ where to run it

llama.cpp · recommended

  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 OpenThinker2 7B · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    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)
    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) ↗

  2. Run OpenThinker2 7B · needs ~4.68 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run OpenThinker2 7B · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/open-thoughts_OpenThinker2-7B-GGUF:Q4_K_M

Official weights and documentation: huggingface.co/open-thoughts ↗

$ how the score was worked out · open-thoughts/OpenThinker2-7B… 99%

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
open-thoughts/OpenThinker2-7B model card · lab-reported5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 49.3

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data updated · sources and licences · json