whatmodel.sh / AceReason-Nemotron-1.1-7B

AceReason-Nemotron-1.1-7B

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AceReason-Nemotron-1.1-7B is an open-weights language model from NVIDIA, released June 2025. Its published size is 7.62B parameters. Licence: nvidia-open-model-license.

Catalogue identity notes

NVIDIA's own model, repo nvidia/AceReason-Nemotron-1.1-7B @2be9ed2 (licence nvidia-open-model-license). 'built upon the Qwen2.5-Math-7B base' (Release Date: June 16, 2025). Printed as 'AceReason-Nemotron-1.1-7B' in the Nemotron-Cascade report (arXiv 2512.13607v2) Table 9; listed so the report's NVIDIA rows join the existing LiveCodeBench Pro 25Q2 runs.

Model information sources

Release date ↗ · Weights licence ↗

safetensors metadata of nvidia/AceReason-Nemotron-1.1-7B @2be9ed2: 7,615,616,512 parameters (dense).

48.7±1.9#368 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of AceReason-Nemotron-1.1-7B, out of 828 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
439GPT-4o Audio Preview (2024-12-17) OpenAI49±2comparison only2024-12
440Claude 3 Sonnet Anthropic49±1comparison only2024-02
441AceReason-Nemotron-1.1-7B NVIDIA49±2local~6GB2025-06
442Mixtral 8x22B Instruct v0.1 Mistral49±1local~56GB2024-04
443Yi-Coder 9B Chat 01.AI49±1local~6.8GB2024-08
more from NVIDIA
98Nemotron 3 Ultra 550B-A55B NVIDIA69±1api $1.35/M +local~89GB2026-06
155Nemotron Cascade 2 30B-A3B NVIDIA66±1local~20GB2026-03
168Nemotron 3 Super 120B-A12B NVIDIA65±1api $0.28/M +local~73GB2026-03

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #311 of 458 · 98% of its score · 3 tests

Beats Solar Open 100B (102B-A12B), GLM-4.7-Flash, Qwen3 4B Instruct 2507 here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Phi-4-mini 3.8B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LiveCodeBench Pro 2025Q2 (Easy) lab42.3% ✎▄·▄····▄▄▄█▄ beats 0/7medium · avg 45%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 46%at its level
LiveCodeBench Pro 2025Q2 (Medium) lab1.8% ✎▃▃····▃·█·▃▃ beats 0/7very hard · avg 2.4%▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · too hard for it
LiveCodeBench v6 (Aug 2024–May 2025) lab55.2% ✎▄▄···▄·▄▄█·▄ beats 0/7medium · avg 57%▇▇▇▇▇▇▇▇▇ 22%at its level

$ 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 AceReason-Nemotron-1.1-7B · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/nvidia_AceReason-Nemotron-1.1-7B-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 AceReason-Nemotron-1.1-7B · 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/nvidia_AceReason-Nemotron-1.1-7B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run AceReason-Nemotron-1.1-7B · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

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 AceReason-Nemotron-1.1-7B · needs ~4.3 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/AceReason-Nemotron-1.1-7B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/AceReason-Nemotron-1.1-7B-4bit

Official weights and documentation: huggingface.co/nvidia ↗

$ how the score was worked out · NVIDIA — Nemotron-Cascade: Sc… 98%

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
NVIDIA — Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models · lab-reported3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%CC BY 4.0
prior · pull toward the middle▇ 2%
weighted average= 48.7

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json