[whatmodel.sh](../../README.md) / Mistral NeMo Instruct (2407)
# Mistral NeMo Instruct (2407)
Mistral · released 2024-07 · 12.25B params · apache-2.0
**score 45.9** ±0.7 · #456 of 732 · from 23 benchmark results across 15 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mistral NeMo Instruct (2407) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #268 of 313 4.3% 3
coding ▁▁▁▃▅▇●▇▅▄▃▁ #274 of 469 4.2% 4
math ▁▁▃●█▆▄█▄▂▁▁ #375 of 431* 15% 2
agent tasks · #268 of 313 · 4.3% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 27.6% ▁▁▁▂█●▅▅▆▂▂▂ beats 22/77 hard · avg 31% 2.0% at its level
Galileo Agent Leaderboard v1 (too… 66.1% ●▃▃▃▆▅▆▅█▂▅▂ beats 1/31 easy · avg 78% 1.2% at its level
BFCL v3 51.5% ▁▁▁▁▁▃●█▅▄▁▁ beats 40/114 medium · avg 51% 1.2% blunt · at its level
coding · #274 of 469 · 4.2% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 52.8% ▃··▁▂▂▃▂●▃▄▂ beats 28/47 medium · avg 51% 1.6% sharp · at its level · noisy
CodeReviewQA: change localisation… 29.0% █▂▁▁▂▂▂▃●▅▅▇ beats 24/47 hard · avg 33% 1.4% sharp · noisy
CodeReviewQA: automated code refi… 33.1% ▁▁▁▁▁▁▂▂▃●▅▄ beats 31/47 hard · avg 32% 1.2% at its level
CodeReviewQA: change type recogni… 75.8% ▁▁▁······▁▃● beats 32/47 easy · avg 67% 0.0% blunt · at its level · noisy
math · #375 of 431 · 15% of its score
Beats [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 82.8% ▁▄·▁▄█●▅▅███ beats 15/43 very easy · avg 87% 9.4% sharp
MATH level 5 10.8% ▂▂●▆▄█▆▃▅▂▃▄ beats 6/92 hard · avg 28% 5.8% sharp
other tests (14)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 66.9% ▁·▁▁▄●▄▅█▄▂▁ beats 9/53 easy · avg 75% 26% at its level
LiveBench 2024-07-26 29.2% ▁▁▁▂▂▃▂●▆▃▅▃ beats 24/61 medium · avg 38% 19% at its level
WildBench v2 WB-Score (task macro) 44.3751 WB-Score ▁·▁▁·▃█▄▇●▅▆ beats 32/52 avg 43.019 WB-Score 5.7% sharp
CPTUB average (SpeakLeash, 0-5 ju… 58.8% ▁▂▂▂▃●▃▆▅▄▂▃ beats 27/95 medium · avg 65% 4.2% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 11.8% ▁▁▃●██▁▁·▁▁▁ beats 13/57 hard · avg 15% 4.2% sharp
EvalPerf correctness (118 tasks, … 57.4% ▂·●▂▂▆▅▆▆▆▃█ beats 1/29 easy · avg 74% 3.6% at its level
GPQA Diamond 29.9% ▁▁▁●▂▄▆▄▅▇█▅ beats 16/211 hard · avg 39% 3.5% sharp
Polish EQ-Bench v2 (SpeakLeash) 61.8% ▁··▁▁▂▂▆▅●█▆ beats 42/70 medium · avg 62% 3.3% at its level
Open Persian LLM Leaderboard v2 a… 46.7% ▂▄▂▃●▄▃▆▃▅▂▂ beats 21/81 medium · avg 57% 2.5% blunt · at its level
RULER 64k 46.8% ●▁·▁·▂▃▄█▂·▁ beats 0/22 easy · avg 82% 1.3% typical
Open PT-LLM Leaderboard average (… 73.8% ▃▁▁▁▁▁▁▁▂█●▁ beats 210/276 medium · avg 72% 1.3% at its level · noisy
EuroEval Life in the UK (English … 84.6% ▁▁▁▁▁▁▁▂▄●▄▃ beats 97/146 easy · avg 82% 0.8% at its level · noisy
RULER 128k 19.0% ●··▂▄▂▆██▄▄▂ beats 0/21 easy · avg 67% 0.4% at its level · noisy
Vectara hallucination leaderboard… 88.8% ▁▁▂●▄▄█▆▃▂▂▁ beats 20/140 very easy · avg 94% 0.4% blunt · maxed out · noisy
source results share of the score
WildEval ZeroEval published results 3 39% ▇▇▇▇▇
LiveBench model judgments 1 19% ▇▇
Epoch AI 2 9% ▇
WildBench v2 leaderboard data 1 6% ▇
SpeakLeash CPTUB 1 4% ▇
CodeReviewQA: The Code Review Comprehen… 4 4% ▇
EvalPlus / EvalPerf published evaluatio… 1 4% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Berkeley Function Calling Leaderboard r… 2 3% ▇
Open Persian LLM Leaderboard v2 1 3% ▇
NVIDIA RULER repository README results … 2 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Galileo Agent Leaderboard 1 1% ▇
EuroEval leaderboards 1 1% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 45.9
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Vectara hallucination leaderboard ([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 Mistral NeMo Instruct (2407) (Q4_K_M, needs ~7.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Nemo-Instruct-2407-GGUF/tree/e0ffb63273e1e4dc60400f08a6d0bf2a99c4be21))
```sh
llama cli -hf lmstudio-community/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13 GB) `llama cli -hf lmstudio-community/Mistral-Nemo-Instruct-2407-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 Mistral NeMo Instruct (2407) (Q4_K_M, needs ~7.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Nemo-Instruct-2407-GGUF/tree/e0ffb63273e1e4dc60400f08a6d0bf2a99c4be21))
```sh
lms get https://huggingface.co/lmstudio-community/Mistral-Nemo-Instruct-2407-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 (~13 GB) `lms get https://huggingface.co/lmstudio-community/Mistral-Nemo-Instruct-2407-GGUF@Q8_0 -y` · 4bit (~6.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Nemo-Instruct-2407-4bit -y` · 8bit (~13 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Nemo-Instruct-2407-8bit -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 Mistral NeMo Instruct (2407) (Q4_0, needs ~7.85 GB, from [ollama.com](https://ollama.com/library/mistral-nemo/tags))
```sh
ollama run mistral-nemo:12b
```
Other versions: Q4_K_M (~7.85 GB) `ollama run mistral-nemo:12b-instruct-2407-q4_K_M` · Q8_0 (~15.2 GB) `ollama run mistral-nemo:12b-instruct-2407-q8_0`
### 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 Mistral NeMo Instruct (2407) (4bit, needs ~6.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mistral-Nemo-Instruct-2407-4bit/tree/647ca0751669b21a364c86ccc5df54c4d7e4e91c))
```sh
mlx_lm.chat --model mlx-community/Mistral-Nemo-Instruct-2407-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Mistral-Nemo-Instruct-2407-4bit
```
Other versions: 8bit (~13 GB, Mac only) `mlx_lm.chat --model mlx-community/Mistral-Nemo-Instruct-2407-8bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
[interactive version](https://whatmodel.sh/model/mistral-nemo-instruct-2407) · [JSON](https://whatmodel.sh/api?format=json&all=1)