Mistral AIFrance· released Apr 2026

Mistral Medium 3.5

Name: Mistral Medium 3.5
Author: Mistral AI

Provisional: 8 comparable tests so far. Ranked once it reaches 6 across 3 counting categories.

Mistral AI, France. One of 2 models from this lab on the Index.

Third-party publishedLow confidence (thin coverage)

Snapshot September 8, 2026

OpenCharts Index

33.7

Provisional: not enough comparable tests or counting categories to rank yet · 8 comparable tests · 1 counting categories

Drop any single test and the Index lands between 33.7 and 39.3.

Needs more results (0 of 8 so far)

Distance to today's frontier on the hardest tests. Not a measure of intelligence.

What it's known for

Mistral Medium 3.5, in 8 results.

Strongest in coding (33.7). Its widest gap is on CritPt, 100.0% behind the best published result. No comparable results yet in knowledge, agentic and long context. Below 40% of the best published results, on average.

Strongest category

Coding

Writing, fixing and shipping real code, judged by tests or users.

33.7

SciCode39.6% · 64% of best
WeirdML43.7% · 47% of best
FrontierCode8.0% · 15% of best
Code Arena (WebDev)1,265 · 9% win rate vs leader

Mean of 4 of the category's 7 comparable tests. Counts toward the Index.

Best single result

Text Arena

Answer the same prompt as a rival model; real users vote blind on which answer they prefer.

1,427

39% expected win rate against the board leader (1,507) · #69 of 92.

Millions of blind votes on real prompts are the market's measure of what people prefer.

Where it trails

CritPt

Solve an unpublished research-level physics problem to a numeric or symbolic answer checked by an official server.

0.0%

100.0% behind the best published result (32.3%) · #72 of 78.

Research physics is far past textbook recall. Only a few models produce anything a physicist would accept.

Not measured yet

No comparable result yet in these 3 categories. A blank is a blank, never a zero, and it does not lower the Index.

Knowledge
Agentic
Long context

Covered, but not averaged into the Index:

Reasoning1 of 6 tests · needs 3 · mean 0.0
Math1 of 5 tests · needs 3 · mean 9.0
Multimodalsingle-test category, shown beside the Index · 68.3
Human preferencesingle-test category, shown beside the Index · 77.3

Every score, with its source

See it at work

What Mistral Medium 3.5 was asked to do, and how it did.

Every test it has taken, by category, with the kind of task it faced, who produced the number and where the result landed against the best published one. The examples are original and illustrative, never items from the datasets themselves.

Human preference · mean 77.3 across 1 comparable test · a single-test category, shown beside the Index and never averaged into it

Text ArenaLeaderboard

Answer the same prompt as a rival model; real users vote blind on which answer they prefer.

1,427

39% expected win rate against the board leader (1,507)
#69 of 92

board 2026-09-02

Source: Arena

Every result is read against the best published one on its test: percent scores as a share of the best above chance, Arena ratings as the expected win rate against the board leader, open-ended values on a log scale. Sitting a harder exam never lowers a model. Every number links to the publisher that produced it.

Head to head

Mistral Medium 3.5 against whoever you pick.

Choose a rival. Every counted test both have taken appears side by side as a share of the best published result on that test, with the category means above. A win is a higher share; a gap under 2 points is a tie, because one item on a 45-problem exam is 2.2 points and publishers' own standard errors are of that order.

Mistral Medium 3.5

provisional·Index 33.7F

0 : 6

wins · 0 ties · 6 shared

GPT-6 Astra

#1·Index 99.0A+

GPT-6 Astra comes out ahead on 6 of the 6 tests both have taken.

Category means

Reasoning
0.0
99.6
Knowledge
—
100.0
Coding
33.7
97.7
Math
9.0
99.8
Agentic
—
98.5
Multimodal
68.3
—
Human preference
77.3
—

Shared tests · biggest gaps first

CritPtReasoning
0.0%
31.7%
−98.2
Code Arena (WebDev)Coding
1,265
1,797
−91.1
ProofBenchMath
9.0%
99.0%
−90.0
FrontierCodeCoding
8.0%
53.3%
−84.6
WeirdMLCoding
43.7%
92.9%
−52.9
SciCodeCoding
39.6%
56.5%
−27.2

Bars are distances to the best published result on each test (percent scores above chance, Arena ratings as win rate against the leader, open-ended values on a log scale). Raw values as the publishers report them.

Open GPT-6 Astra

Scores

Every test, every source

The best published run per benchmark, the raw value as the publisher reports it, who produced the number, how close it comes to the best published result on that test, and where the model sits among every model scored on it. Percent scores are read above chance, Arena ratings as a win rate against the board leader, open-ended values on a log scale. A test with too few models to compare against is shown but not counted.

Category means

Reasoning0.0 · 1/6
Knowledge—
Coding33.7 · 4/7
Math9.0 · 1/5
Agentic—
Multimodalbeside68.3 · 1/1
Human preferencebeside77.3 · 1/1
Long context—

Mean · tests taken of the category's comparable tests. A faded category either rests on fewer than half of them or is a single-test category (marked “beside”), which is shown next to the Index and never averaged into it.

CritPtLeaderboardReasoning

0.0%

0.0% of the best (32.3%)
0th percentile of 78

Source: Epoch AI — AI Benchmarking Hub

SciCodeLeaderboardCoding

39.6%

63.8% of the best (62.0%)
17th percentile of 73 · n = 338

Source: Epoch AI — AI Benchmarking Hub

WeirdMLLeaderboardCoding

43.7%

47.1% of the best (92.9%)
19th percentile of 69

Source: Epoch AI — AI Benchmarking Hub