[whatmodel.sh](../../README.md) / Mistral Large 2 (2407)
# Mistral Large 2 (2407)
Mistral · released 2024-07 · 122.61B params · mistral-research
**score 55.0** ±0.5 · #282 of 732 · from 34 benchmark results across 16 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mistral Large 2 (2407) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #234 of 313* 1.1% 2
coding ▁▁▁▃▅▇●▇▅▄▃▁ #227 of 469 16% 5
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #213 of 402 9.3% 3
math ▁▁▃▅●▆▄█▄▂▁▁ #280 of 431 19% 6
agent tasks · #234 of 313 · 1.1% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 57.7% ▁▁▁▁▁▃▅●▅▄▁▁ beats 71/114 medium · avg 51% 0.6% blunt · at its level
BFCL (version not stated) 85.1% ▄▆▄▂·█●·▄··▂ beats 13/17 easy · avg 81% 0.5% blunt · small run
coding · #227 of 469 · 16% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 75.1% ▁▂▆▃▆█●▃▂·▁▁ beats 37/47 medium · avg 57% 6.2% sharp · at its level
Aider code editing (133 Exercism … 60.2% ▁·▁▂▄▁▆●█▆▂▂ beats 30/62 medium · avg 51% 4.9% sharp · at its level
HumanEval (Vector Institute, Insp… 86.6% ▄·▄·●·█·█·▄▄ beats 3/9 easy · avg 77% 2.3% sharp
WebApp1K-Duo-React pass@1 (onekq-… 44.9% ▁··▁·▁●▁▆█▇█ beats 5/45 hard · avg 29% 1.6% at its level
WebApp1K-React pass@1 (onekq-ai) 78.0% ▃▁▁█▁▄▅▃●▇▁▄ beats 20/31 medium · avg 40% 1.2% sharp · at its level · noisy
chat & writing · #213 of 402 · 9.3% of its score
Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Granite 4.0 H Micro 3B](granite-4-0-h-micro.md), [Gemma 3n E4B](gemma-3n-e4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (v0.1, GPT-4-1106 judg… 70.4% ▂▁▁▅█▅▄▃●▅▃▁ beats 46/60 medium · avg 47% 6.0% sharp · at its level
LMArena (text) 1314.4 ▁▁▁▂▃▃▃●▆▇█▃ beats 145/385 avg 1260 2.8% blunt
IFEval (aggregation unspecified) 87.8% ▁▂▄▂▂▄▆●█▅▂▁ beats 91/145 easy · avg 79% 0.5% small run
math · #280 of 431 · 19% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Tulu 2 DPO 13B](tulu-2-dpo-13b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 44.8% ▂▂▅▆▄●▆▃▅▂▃▄ beats 37/92 hard · avg 28% 6.2% sharp · at its level
MATH (Vector Institute, Inspect E… 65.7% ▄··█●▄··▄·▄█ beats 3/9 medium · avg 50% 4.2% sharp · at its level
MATH Level 5 (ZeroEval, 721 probl… 48.5% ▁▁▁▆█▂●▇▄··▂ beats 25/38 hard · avg 30% 3.7% sharp · at its level
GSM8K 91.0% ▂▂▂▂▃▄▅▇██●▄ beats 181/206 easy · avg 82% 2.0% sharp · small run
GSM8K (ZeroEval, zero-shot) 95.5% ▁▄·▁▄█▅▅▅█●█ beats 37/43 very easy · avg 87% 1.9% sharp · maxed out
OTIS mock AIME 2024–2025 8.5% ▃▃▄●▃▃▇▇█▃▂▅ beats 48/197 very hard · avg 7.2% 1.4% sharp · noisy
other tests (18)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 48.0% ▁▁▁▂▂▃▂█▆▃●▃ beats 50/61 medium · avg 38% 12% at its level
MMLU-Redux (ZeroEval, 2,778 quest… 83.0% ▁·▁▁▄▄▄▅●▄▂▁ beats 39/53 easy · avg 75% 10% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 29.0% ▁▁▃▆█●▁▁·▁▁▁ beats 47/57 hard · avg 15% 5.3% sharp · at its level
GPQA Diamond 49.0% ▁▁▁▁▂▄●▄▅▇█▅ beats 66/211 hard · avg 39% 4.1% sharp · at its level
GPQA Diamond (Vector Institute, I… 47.3% ▃··▃▃●▃·▃·▃█ beats 3/9 hard · avg 40% 3.2% sharp · at its level
WildBench v2 WB-Score (task macro) 55.5683 WB-Score ▁·▁▁·▃█▄▇▆▅● beats 49/52 avg 43.019 WB-Score 2.9% sharp
GPQA (subset unspecified) 40.7% ▂▁▂▂▄▃●▃▅█▃▃ beats 41/106 very hard · avg 35% 2.7% sharp · small run
MMLU-Pro (Vector Institute, Inspe… 69.4% ▃····▃●·▃▅·█ beats 3/9 medium · avg 58% 2.4% at its level
BIG-Bench Hard 70.8% ▁▁▁▁▂▄▅▆●▃▃▁ beats 117/155 medium · avg 61% 2.2% sharp · at its level · small run
CPTUB average (SpeakLeash, 0-5 ju… 78.7% ▁▂▂▂▃█▃▆▅●▂▃ beats 78/95 medium · avg 65% 1.8% at its level
MMLU 82.5% ▁▁▁▁▁▂▂▅██●▄ beats 177/214 easy · avg 75% 1.6% small run
MMLU-Pro 54.2% ▁▁·▁▁▁▃●▄▅█▁ beats 54/188 medium · avg 46% 1.4% at its level · small run
Polish EQ-Bench v2 (SpeakLeash) 78.1% ▁··▁▁▂▂▆▅▆█● beats 70/70 medium · avg 62% 1.4% at its level
EvalPerf correctness (118 tasks, … 82.4% ▂·▂▂▂▆▅▆▆●▃█ beats 19/29 easy · avg 74% 1.3% typical
Open Persian LLM Leaderboard v2 a… 64.8% ▂▄▂▃█▄▃▆●▅▂▂ beats 63/81 medium · avg 57% 1.2% blunt · at its level
RULER 64k 78.8% ▁▁·▁·▂●▄█▂·▁ beats 6/22 easy · avg 82% 0.4% typical
ARC Challenge 65.0% ▄▁▁▂▁▄█▇●▂▃▂ beats 112/140 easy · avg 76% 0.3% sharp · noisy · small run
RULER 128k 23.7% ●··▂▄▂▆██▄▄▂ beats 1/21 easy · avg 67% 0.2% at its level · noisy
source results share of the score
WildEval ZeroEval published results 5 27% ▇▇▇
Vector Institute Evaluation Leaderboard 4 12% ▇
LiveBench model judgments 1 12% ▇
Epoch AI 3 12% ▇
Jamba-1.5: Hybrid Transformer-Mamba Mod… 8 11% ▇
Arena-Hard-Auto v0.1 leaderboard 1 6% ▇
Aider code editing benchmark runs 1 5% ▇
WildBench v2 leaderboard data 1 3% ▇
WebApp1K models leaderboard 2 3% ▇
LMArena leaderboard dataset 1 3% ▇
SpeakLeash CPTUB 1 2% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
EvalPlus / EvalPerf published evaluatio… 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
NVIDIA RULER repository README results … 2 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.0
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Vector Institute Evaluation Leaderboard ([Apache-2.0](../../licenses/vector-inspect-evals-leaderboard-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/)) · Jamba-1.5: Hybrid Transformer-Mamba Models at Scale ([CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt))
## 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 Large 2 (2407) (Q4_K_M, needs ~73.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2407-GGUF/tree/e0b81849619f12013742b7b930207f8664b26c6e))
```sh
llama cli -hf lmstudio-community/Mistral-Large-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-Large-Instruct-2407-GGUF:Q4_K_M
```
Other versions: IQ2_M (~41.6 GB) `llama cli -hf lmstudio-community/Mistral-Large-Instruct-2407-GGUF:IQ2_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 Mistral Large 2 (2407) (Q4_K_M, needs ~73.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2407-GGUF/tree/e0b81849619f12013742b7b930207f8664b26c6e))
```sh
lms get https://huggingface.co/lmstudio-community/Mistral-Large-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: IQ2_M (~41.6 GB) `lms get https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2407-GGUF@IQ2_M -y` · 4bit (~69 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Large-Instruct-2407-4bit -y` · 8bit (~130.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Large-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 Large 2 (2407) (Q4_K_M, needs ~74.07 GB, from [ollama.com](https://ollama.com/library/mistral-large/tags))
```sh
ollama run mistral-large:123b-instruct-2407-q4_K_M
```
Other versions: Q8_0 (~147.63 GB) `ollama run mistral-large:123b-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 Large 2 (2407) (4bit, needs ~69 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mistral-Large-Instruct-2407-4bit/tree/1b496d417b097d407929998529dec7f2152c27f0))
```sh
mlx_lm.chat --model mlx-community/Mistral-Large-Instruct-2407-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Mistral-Large-Instruct-2407-4bit
```
Other versions: 8bit (~130.3 GB, Mac only) `mlx_lm.chat --model mlx-community/Mistral-Large-Instruct-2407-8bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407)
[interactive version](https://whatmodel.sh/model/mistral-large-2407) · [JSON](https://whatmodel.sh/api?format=json&all=1)