[whatmodel.sh](../../README.md) / Mistral Large 2 (2411)
# Mistral Large 2 (2411)
Mistral · released 2024-11 · 123B params · mistral-research
**score 55.8** ±0.9 · #263 of 732 · from 18 benchmark results across 14 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 (2411) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #233 of 313 7.1% 3
coding ▁▁▁▃▅▇●▇▅▄▃▁ #220 of 469* 15% 1
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #222 of 402* 8.4% 1
math ▁▁▃▅●▆▄█▄▂▁▁ #265 of 431* 23% 2
agent tasks · #233 of 313 · 7.1% 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 V4 (December 2025 archive) 38.4% ▁▁▁▂██●▅▆▂▂▂ beats 44/77 hard · avg 31% 3.9% at its level
BFCL v3 58.0% ▁▁▁▁▁▃▅█●▄▁▁ beats 73/114 medium · avg 51% 1.8% blunt · at its level
Galileo Agent Leaderboard v1 (too… 81.0% ▃▃▃▃●▅▆▅█▂▅▂ beats 10/31 easy · avg 78% 1.3% typical
coding · #220 of 469 · 15% 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
Aider code editing (133 Exercism … 65.4% ▁·▁▂▄▁▆▄●▆▂▂ beats 35/62 medium · avg 51% 15% sharp · at its level
chat & writing · #222 of 402 · 8.4% 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
LMArena (text) 1305.6 ▁▁▁▂▃▃▃●▆▇█▃ beats 133/385 avg 1260 8.4% blunt
math · #265 of 431 · 23% of its score
Beats [GPT-4o (2024-11-20)](gpt-4o-2024-11-20.md), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md), [Llama 3.1 405B Instruct](llama-3-1-405b.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), [CodeGemma 7B (v1.0)](codegemma-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 50.3% ▂▂▅▆▄●▆▃▅▂▃▄ beats 43/92 hard · avg 28% 18% sharp · at its level
OTIS mock AIME 2024–2025 7.8% ▃▃▄●▃▃▇▇█▃▂▅ beats 44/197 very hard · avg 7.2% 4.9% sharp · noisy
other tests (11)
test result vs the others in its run difficulty counts why
GPQA Diamond 51.3% ▁▁▁▁▂▄●▄▅▇█▅ beats 72/211 hard · avg 39% 13% sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju… 80.1% ▁▂▂▂▃█▃▆▅●▂▃ beats 81/95 medium · avg 65% 5.4% at its level
EQ-Bench 3 (emotional intelligenc… 1108.9 ▁·▂▁▄●▅▃█▄▁▁ beats 13/62 avg 1034 5.2% typical
BRIDGE clinical text, zero-shot a… 42.3% ▁▁▁▁▁▂▂▃▄▆●▃ beats 87/97 hard · avg 31% 5.1% blunt · at its level
AraGen-v3 3C3H score (Inception, … 52.0% ▁▁·▁▂▂▄█▄●▄▄ beats 47/66 hard · avg 29% 5.0% at its level
Polish EQ-Bench v2 (SpeakLeash) 77.3% ▁··▁▁▂▂▆▅▆█● beats 68/70 medium · avg 62% 4.1% at its level
Open Persian LLM Leaderboard v2 a… 64.3% ▂▄▂▃█▄▃▆●▅▂▂ beats 61/81 medium · avg 57% 3.7% blunt · at its level
Judgemark v2 (LLM-as-judge for cr… 65.6% ▃▃··▃▅●···█▃ beats 6/10 medium · avg 53% 2.8% at its level
RULER 64k 85.9% ▁▁·▁·▂▃▄●▂·▁ beats 17/22 easy · avg 82% 1.3% typical
RULER 128k 48.1% ▄··▂●▂▆██▄▄▂ beats 4/21 easy · avg 67% 0.5% noisy
Vectara hallucination leaderboard… 95.5% ▁▂▁▂▅█▂▃▄●▁▁ beats 93/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 3 36% ▇▇▇▇
Aider code editing benchmark runs 1 15% ▇▇
LMArena leaderboard dataset 1 8% ▇
Berkeley Function Calling Leaderboard r… 2 6% ▇
SpeakLeash CPTUB 1 5% ▇
EQ-Bench 3 canonical leaderboard results 1 5% ▇
BRIDGE Medical Leaderboard 1 5% ▇
Inception Arabic/Hindi leaderboards 1 5% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 4% ▇
Open Persian LLM Leaderboard v2 1 4% ▇
Judgemark v2 results 1 3% ▇
NVIDIA RULER repository README results … 2 2% ▇
Galileo Agent Leaderboard 1 1% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.8
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Judgemark v2 results ([MIT](../../licenses/judgemark-v2-MIT.txt)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · 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 Large 2 (2411) (Q4_K_M, needs ~73.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2411-GGUF/tree/cd165d3f726c22ec148ec99736e025d59b81ed6f))
```sh
llama cli -hf lmstudio-community/Mistral-Large-Instruct-2411-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-2411-GGUF:Q4_K_M
```
Other versions: Q8_0 (~130.3 GB) `llama cli -hf lmstudio-community/Mistral-Large-Instruct-2411-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 Large 2 (2411) (Q4_K_M, needs ~73.2 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2411-GGUF/tree/cd165d3f726c22ec148ec99736e025d59b81ed6f))
```sh
lms get https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2411-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 (~130.3 GB) `lms get https://huggingface.co/lmstudio-community/Mistral-Large-Instruct-2411-GGUF@Q8_0 -y` · 8bit (~137.9 GB, Mac only) `lms get https://huggingface.co/sjug/Mistral-Large-Instruct-2411-8bit -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 (2411) (Q4_K_M, needs ~74.3 GB, from [ollama.com](https://ollama.com/library/mistral-large/tags))
```sh
ollama run mistral-large:123b-instruct-2411-q4_K_M
```
### 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 (2411) (8bit, needs ~137.9 GB, Mac only, from [huggingface.co/sjug](https://huggingface.co/sjug/Mistral-Large-Instruct-2411-8bit/tree/35446b556a06f09c48633c186990564d87a2b72c))
```sh
mlx_lm.chat --model sjug/Mistral-Large-Instruct-2411-8bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model sjug/Mistral-Large-Instruct-2411-8bit
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411)
[interactive version](https://whatmodel.sh/model/mistral-large-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)