Mistral 7B Instruct v0.1 — Benchmarks

Benchmark scores for Mistral 7B Instruct v0.1 aggregated from public leaderboards (3 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

No llmrun Composite Score for Mistral 7B Instruct v0.1: the score is only published for a model measured on at least 4 of the boards in the score panel, spanning at least 2 benchmark categories and including a reasoning or coding board — or on 3 boards that narrow its score to a 90% range of at most 15.6 points. Either way the results must come from at least two different benchmarks. Its individual scores below stand on their own — see how the llmrun Composite Score gets calculated.

Mistral 7B Instruct v0.1 reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

PIQA
PIQA (Physical Interaction QA) asks a model to choose the more sensible of two ways to accomplish an everyday physical task, testing practical, physical-world commonsense that isn't well captured by purely textual reasoning tests.
82.2%
82.2%
median 80.1%open leader 88.6%
Overall best: GPT 4o Mini (Jul 18, 2024) 88.7%
68th percentile · #13 of 40 open · #23 of 59 all
#13 of 40
#23 of 59
Lab-reported
Epoch AI · Sep 2023

Mistral 7B Instruct v0.1 knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

MMLU-Pro
MMLU-Pro is a harder, cleaned-up successor to MMLU with ten answer choices and more reasoning-heavy questions across 14 subjects, measuring broad knowledge and reasoning together.
25.8%
25.8%
median 52.0%open leader 88.0%
Overall best: Gemini-3.1-Pro 91.2%
14th percentile · #125 of 146 open · #232 of 257 all
#125 of 146
#232 of 257
Independent
TIGER-Lab

Mistral 7B Instruct v0.1 instruction following benchmarks

How precisely a model obeys explicit, verifiable instructions — format, length, and content constraints — and, more broadly, how much people prefer its responses head-to-head in blind human voting.

LMArena Text
Arena (arena.ai, formerly LMArena / Chatbot Arena) ranks models by millions of blind, head-to-head human votes on real prompts. The Arena Score shown here is a Bradley-Terry rating computed with Arena's default style control, so higher means people preferred that model's answers more often — it's a relative rating, not a percentage, so compare it only to other models on this board.
1110.4
1110.4
median 1321.4open leader 1488.2
Overall best: Gemini 4 Carbon (High) 1527.5
3rd percentile · #183 of 188 open · #391 of 408 all
#183 of 188
#391 of 408
Arena
Arena · Oct 2026

Scores aggregated from public benchmark sources (Epoch AI: 1 board, Arena: 1 board, TIGER-Lab: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.