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Mistral 7B Instruct v0.1

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Mistral 7B Instruct v0.1 is an open-weights language model from Mistral, released September 2023. Its published size is 7.24B parameters and a 8k-token context window. Licence: apache-2.0.

Catalogue identity notes

Arena entry 'mistral-7b-instruct' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'mistral-7b-instruct' as 'Mistral-7B-Instruct-v0.1' (Mistral, Apache 2.0), linking https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1. Maker repo mistralai/Mistral-7B-Instruct-v0.1 @ec5deb6: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-09: month the maker's Hugging Face repo was created (createdAt 2023-09-27). Arena key 'mistral-7b-instruct'; LMArena's table labels it 'Mistral-7B-Instruct-v0.1'. ollama 'mistral:v0.1' is the same digest as mistral:7b-instruct-q4_0.

32.0±1.0#626 of 719 LLMsfrom 12 benchmark results across 8 sources

$ how it compares

The LLMs ranked either side of Mistral 7B Instruct v0.1, out of 828 with a score, then more from Mistral.

#modelscorewhereRAMreleased
721Orca 2 13B Microsoft32±2local~9.8GB2023-11
722Gemma 7B IT Google32±1local~7.1GB2024-02
723Mistral 7B Instruct v0.1 Mistral32±1local~6.1GB2023-09
724Qwen1.5 4B Chat Alibaba32±2local~4.1GB2024-02
725Ghost 7B Alpha Ghost X32±2local~6.1GB2024-04
more from Mistral
79Mistral Large 4 (public preview) Mistral71±2api $2.06/M2026-10
174Mistral Medium 3.5 (128B) Mistral65±1api $3.00/M +local~42GB2026-04
180Mistral Medium 3.1 (2508) Mistral64±2comparison only2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #420 of 458 * · 36% of its score · 1 test

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Llama 3.2 1B Instruct, SmolLM2 1.7B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval28.7% ✎▁█▄▅▇█·▄▇▅▄▄ beats 3/42easy · avg 78%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 36%sharp · at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #376 of 393 * · 17% of its score · 2 tests

Beats Phi-2 (2.7B), Nous-Hermes-13b here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1008.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 16/95avg 1201▇▇▇ 8.7%blunt
LMArena (text)1110.3 ✎▁▁▁▃▃▃▃▆▆▇█▃ beats 17/379avg 1268▇▇▇ 8.6%blunt
other tests9 not in a task · 47% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)17.6% ✎▂▃▃▄▄█▁▄▅▃▁▃ beats 2/42medium · avg 55%▇▇▇▇▇▇▇ 17%typical
Natural Questions (closed book)17.8% ✎▁·▁·▁▂▁▂·▅▂█ beats 14/28medium · avg 58%▇▇▇▇▇ 12%typical
MMLU-Pro24.2% ✎▃▂▅█▃·█▃▅··▂ beats 0/23medium · avg 57%▇▇▇▇ 9.0%typical
Open Persian LLM Leaderboard v2 average (21 tasks)36.5% ✎▂▄▂▃█▄▃▆▃▅▂▂ beats 8/81medium · avg 58%▇ 3.5%blunt · at its level
TriviaQA52.2% ✎▁··▁█▃▃▁▄▅▄█ beats 20/28easy · avg 79%▇ 1.5%at its level · noisy
Open PT-LLM Leaderboard average (9 Portuguese tasks)59.7% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 88/276medium · avg 73%▇ 1.5%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy69.0% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 37/146easy · avg 83%▇ 1.3%at its level · noisy
TruthfulQA MC255.9% ✎▅▃█▆▁▂▅···▁▁ beats 28/31medium · avg 64%▇ 0.7%blunt · at its level
1 more test
testresultvs the others in its rundifficultycountswhy
HaluEval QA hallucination detection (accuracy)49.2% ✎█▁▁·▁▁·▁▁▁▁▁ beats 4/27medium · avg 70%▇ 0.7%sharp · too hard for it · noisy

$ 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 Mistral 7B Instruct v0.1 · needs ~4.4 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/Mistral-7B-Instruct-v0.1-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 Mistral 7B Instruct v0.1 · needs ~4.4 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Mistral 7B Instruct v0.1 · needs ~4.84 GB · from ollama.com
    chat in the terminal
    ollama run mistral:v0.1

Weights and documentation: huggingface.co/mistralai ↗

$ how the score was worked out · Top Leaderboard Ranking = Top… 53%, Hallucinations Leaderboard re… 14%, MMLU-Pro author-released eval… 9% and 5 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

sourceresultsshare of the scorelicence
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%CC BY 4.0
Hallucinations Leaderboard results (HF dataset hallucinations-leaderboard/results)4▇▇▇▇ 14%Apache-2.0
MMLU-Pro author-released evaluation runs1▇▇▇ 9%Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇ 9%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇ 9%CC BY 4.0
Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab)1▇ 3%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard1▇ 1%MIT
prior · pull toward the middle▇ 0%
weighted average= 32.0

$ improve this page

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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