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OpenThinker 7B

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

Catalogue identity notes

open-thoughts/OpenThinker-7B @4ba9dee (created 2025-01-28, licence apache-2.0). Open Thoughts' first-generation reasoning model fine-tuned from Qwen2.5-7B-Instruct on OpenThoughts-114k (Jan/Feb 2025). Rows from Open Thoughts' Evalchemy tables (own cards and the OpenThinker2 cards), with catalogued comparators (DeepSeek-R1-Distill-Qwen-7B, GPT-4o 0513, o1-mini).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 7,615,616,512 parameters (open-thoughts/OpenThinker-7B @4ba9dee).

45.2±1.6#440 of 719 LLMsfrom 9 benchmark results across 2 sources

$ how it compares

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

#modelscorewhereRAMreleased
524Ministral 3 3B Mistral45±1api $0.10/M +local~3.5GB2025-12
525Claude 2.0 Anthropic45±1comparison only2023-07
526OpenThinker 7B Open Thoughts45±2local~6GB2025-01
527Phi-3 Medium 128K Instruct Microsoft45±1local~9.8GB2024-04
528QwQ-32B-Preview Alibaba45±2local~21GB2024-11
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▁▁▁▃▆██▇▆▄▃▁ #424 of 458 * · 13% of its score · 1 test

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Llama 3.2 1B Instruct, SmolLM2 1.7B Instruct, Mistral 7B Instruct v0.1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LiveCodeBench v2 lab +139.9% ✎▄··█·▄···▄▄█ beats 0/7medium · avg 50%▇▇▇▇▇ 13%at its level · several runs
math▁▂▃▅▆▅▄█▄▁▁▁ #228 of 398 · 56% of its score · 3 tests

Beats GPT-4o (2024-08-06), Command A (03-2025), GPT-4o (2024-11-20) here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
AIME 2025 lab23.3% ✎▁·▁▁▁▃▅▆█▇▅▁ beats 13/133medium · avg 38%▇▇▇▇▇▇▇▇ 20%sharp · at its level · small run
AIME 2024 lab +131.3% ✎▁▁▁▂▁▃▃▅███▃ beats 17/85medium · avg 53%▇▇▇▇▇▇▇▇ 19%sharp · at its level · small run · several runs
MATH-500 lab +183.0% ✎▁▃▂▃▅▇▅▇█▅▂▁ beats 25/84very easy · avg 89%▇▇▇▇▇▇▇ 17%small run · several runs
other tests1 not in a task · 30% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond lab +142.4% ✎▁▁▁▁▂▄▅▅███▄ beats 60/310hard · avg 50%▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · small run · several runs

$ where to run it

llama.cpp · recommended

version

  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 OpenThinker 7B · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    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)
    llama serve -hf bartowski/OpenThinker-7B-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run OpenThinker 7B · needs ~4.7 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/OpenThinker-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 OpenThinker 7B · needs ~4.7 GB · from ollama.com
    chat in the terminal
    ollama run openthinker:7b

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run OpenThinker 7B · needs ~4.3 GB, Mac only · from huggingface.co/AlejandroOlmedo
    chat in the terminal
    mlx_lm.chat --model AlejandroOlmedo/OpenThinker-7B-4bit-mlx
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model AlejandroOlmedo/OpenThinker-7B-4bit-mlx

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

$ how the score was worked out · open-thoughts/OpenThinker2-7B… 78%, open-thoughts/OpenThinker-7B… 21%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 78%Apache-2.0
open-thoughts/OpenThinker-7B model card · lab-reported4▇▇▇▇▇▇ 21%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 45.2

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