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Mistral 7B Instruct v0.2

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Mistral 7B Instruct v0.2 is an open-weights language model from Mistral, released December 2023. Its published size is 7.24B parameters and a 32k-token context window. Licence: apache-2.0.

Catalogue identity notes

Arena entry 'mistral-7b-instruct-v0.2' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'mistral-7b-instruct-v0.2' as 'Mistral-7B-Instruct-v0.2' (Mistral, Apache-2.0), linking https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2. Maker repo mistralai/Mistral-7B-Instruct-v0.2 @63a8b08: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-12: month the maker's Hugging Face repo was created (createdAt 2023-12-11).

35.9±0.5#592 of 719 LLMsfrom 24 benchmark results across 15 sources

$ how it compares

The LLMs ranked either side of Mistral 7B Instruct v0.2, out of 828 with a score, then more from Mistral.

#	model	score	where	RAM	released
684	LFM2 700M Liquid AI	36±2	local	~1.9GB	2025-07
685	Granite 3.2 2B Instruct IBM	36±1	local	~3GB	2025-02
686	Mistral 7B Instruct v0.2 Mistral	36±1	local	~6GB	2023-12
687	WizardLM 13B V1.2 Microsoft	36±1	local	~9.8GB	2023-07
688	Tulu 2 7B Allen Institute for AI	36±2	local	~5.8GB	2023-11
	more from Mistral
79	Mistral Large 4 (public preview) Mistral	71±2	api $2.06/M		2026-10
174	Mistral Medium 3.5 (128B) Mistral	65±1	api $3.00/M +local	~42GB	2026-04
180	Mistral Medium 3.1 (2508) Mistral	64±2	comparison only		2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #406 of 458 · 63% of its score · 6 tests

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails SmolLM2 1.7B Instruct, DeepSeek LLM 7B Chat, Qwen2.5 1.5B Instruct here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
HumanEval +1	42.1% ✎	▁·▁▁▃▄▆▅█▃▁▁ beats 24/99	easy · avg 79%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%	sharp · at its level · several runs
HumanEval+	36.0% ✎	▁·▁▁▁▃▄▄▄█▃▁ beats 25/99	easy · avg 74%	▇▇▇▇▇▇ 16%	sharp · at its level
HumanEvalSynthesize (HumanEvalPack, 6-language average)	30.7% ✎	▁·▂▁▂▆▅█▄▄▁▂ beats 7/35	medium · avg 64%	▇▇ 4.2%	at its level
MBPP+	37.0% ✎	▁···▁▁▃▅▇█▆▂ beats 6/75	easy · avg 68%	▇ 3.1%	at its level
HumanEvalExplain (HumanEvalPack, 6-language average)	24.1% ✎	▄▇█▃▄▇▄▁▇▃·▁ beats 19/35	medium · avg 49%	▇ 2.3%	typical
HumanEvalFix (HumanEvalPack, 6-language average)	26.8% ✎	▁·▁▁▄██▄█▁▅▅ beats 21/35	medium · avg 56%	▇ 2.0%	sharp

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #345 of 393 · 11% of its score · 4 tests

Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-3-mini-4k-instruct (June 2024 update) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 H 350M here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
AlpacaEval 2 length-controlled	17.1% ✎	▁▂▂▃▆▆█▆▄▄▁▁ beats 43/122	medium · avg 39%	▇▇ 4.7%	typical
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)	1054.0 ✎	▂▁▂▁▃▇█▆▇▄▂▂ beats 27/95	avg 1201	▇ 2.5%	blunt
LMArena (text)	1149.3 ✎	▁▁▁▂▃▃▃▆▆▇█▃ beats 38/379	avg 1268	▇ 2.5%	blunt
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)	92.8% ✎	▂▂·▆·▇▃▆█▆▄▂ beats 36/45	very easy · avg 96%	▇ 1.1%	sharp · noisy

other tests13 not in a task · 27% of its score

test	result	vs the others in its run	difficulty	counts	why
LiveBench 2024-07-26	20.1% ✎	▁▁▁▂▂▃▂█▆▃▅▃ beats 15/61	medium · avg 41%	▇▇ 6.2%	typical
EvoEval (overall)	14.7% ✎	▃▂▃▄▄█▁▄▅▃▁▃ beats 1/42	medium · avg 55%	▇▇ 5.9%	at its level
Natural Questions (closed book)	24.7% ✎	▁·▁·▁▂▁▂·▅▂█ beats 19/28	medium · avg 58%	▇▇ 3.9%	at its level
MMLU-Pro	28.8% ✎	▃▃▅█▃·█▃▅··▂ beats 2/23	medium · avg 57%	▇ 3.1%	typical
WildBench v2 WB-Score (task macro)	25.6337 WB-Score ✎	▁·▁▁·▃█▄▇▆▅▆ beats 8/52	avg 45.199 WB-Score	▇ 2.8%	sharp
Polish EQ-Bench v2 (SpeakLeash)	53.3% ✎	▁··▁▁▂▂▆▅▆█▆ beats 27/70	medium · avg 64%	▇ 1.3%	at its level
Open Persian LLM Leaderboard v2 average (21 tasks)	39.4% ✎	▂▄▃▃█▄▃▆▃▅▂▂ beats 13/81	medium · avg 58%	▇ 1.0%	blunt · at its level
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)	0.5% ✎	▃▁·▁▂▂▄█▄▅▄▄ beats 0/66	hard · avg 31%	▇ 0.5%	typical

5 more tests

test	result	vs the others in its run	difficulty	counts	why
Open PT-LLM Leaderboard average (9 Portuguese tasks)	66.7% ✎	▃▁▁▁▁▁▁▁▂█▅▁ beats 128/276	medium · avg 73%	▇ 0.5%	at its level · noisy
TriviaQA	63.9% ✎	▁··▁█▃▃▁▄▅▄█ beats 23/28	easy · avg 79%	▇ 0.4%	at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy	65.4% ✎	▁▁▁▁▁▁▁▃▄█▄▃ beats 35/146	easy · avg 83%	▇ 0.4%	at its level · noisy
HaluEval QA hallucination detection (accuracy)	23.0% ✎	█▁▁·▁▁·▁▁▁▁▁ beats 3/27	medium · avg 70%	▇ 0.3%	sharp · noisy
TruthfulQA MC2	66.8% ✎	▅▃█▆▁▂▅···▃▁ beats 30/31	medium · avg 64%	▇ 0.2%	blunt · at its level

$ where to run it

llama.cpp · recommended

version

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 ↗
Run Mistral 7B Instruct v0.2 · needs ~4.4 GB · from huggingface.co/TheBloke
chat in the terminal
llama cli -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run Mistral 7B Instruct v0.2 · needs ~4.4 GB · from huggingface.co/TheBloke
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF@Q4_K_M -y
Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

Install Ollama
download it (macOS / Windows) ↗
Linux
curl -fsSL https://ollama.com/install.sh | sh
Run Mistral 7B Instruct v0.2 · needs ~4.84 GB · from ollama.com
chat in the terminal
ollama run mistral:7b-instruct-v0.2-q4_K_M

MLX (Mac)

Install MLX (Mac) · Apple silicon Macs only, text only.
with uv
uv tool install mlx-lm
or
pip install mlx-lm
Run Mistral 7B Instruct v0.2 · needs ~4.3 GB, Mac only · from huggingface.co/mlx-community
chat in the terminal
mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.2-4bit
Or start an OpenAI-compatible server at http://localhost:8080/v1
mlx_lm.server --model mlx-community/Mistral-7B-Instruct-v0.2-4bit

Weights and documentation: huggingface.co/mistralai ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 42%, Top Leaderboard Ranking = Top… 18%, Granite Code Models: A Family… 9% and 12 more

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

source	results	share of the score	licence
EvalPlus / EvalPerf published evaluation runs	3	▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%	Apache-2.0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs	2	▇▇▇▇▇ 18%	CC BY 4.0
Granite Code Models: A Family of Open Foundation Models for Code Intelligence — authors' own runs	3	▇▇▇ 9%	CC BY 4.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets	1	▇▇ 6%	Apache-2.0 (stated in the LiveBench datasheet)
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)	2	▇▇ 6%	Apache-2.0
Hallucinations Leaderboard results (HF dataset hallucinations-leaderboard/results)	4	▇ 5%	Apache-2.0
MMLU-Pro author-released evaluation runs	1	▇ 3%	Apache-2.0
WildBench v2 leaderboard data (Ai2)	1	▇ 3%	Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)	1	▇ 2%	CC-BY-4.0
LMArena leaderboard dataset	1	▇ 2%	CC BY 4.0
SpeakLeash Polish EQ-Bench leaderboard	1	▇ 1%	Apache-2.0
Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab)	1	▇ 1%	Apache-2.0
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)	1	▇ 1%	Apache-2.0
Open Portuguese LLM Leaderboard (archived)	1	▇ 0%	Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard	1	▇ 0%	MIT
prior · pull toward the middle		▇ 0%
weighted average		= 35.9