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GPT-4o (2024-08-06) vs Mistral Medium 3.1

Mistral Medium 3.1 leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 9.2 to 7.7, and Mistral Medium 3.1 costs 5.0x less per output token ($2.00 vs $10.00 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Mistral Medium 3.1
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
5
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
No data
Output $/M
$2.00
43
vs boards won of 48 shared

GPT-4o (2024-08-06) or Mistral Medium 3.1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4o (2024-08-06) for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
48
GPT-4o (2024-08-06) only
10
Mistral Medium 3.1 only
26
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4o (2024-08-06) not benchmarked, Mistral Medium 3.1 not benchmarked.
Widest category
General, 22.0 points apart on average over 21 shared benchmarks.
Widest single gap
LMArena Text: medicine and healthcare: GPT-4o (2024-08-06) 1252 against Mistral Medium 3.1 1449, 32.9 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Mistral Medium 3.1 $2.00 per million output tokens, 5.0x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Mistral Medium 3.1 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4o (2024-08-06)BarsMistral Medium 3.1Ties
Coding04
Intelligence01
Agentic work01
Reasoning15
Math05
Knowledge46
General021
All shared boards543
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
AA Coding IndexNot measured20.5Only Mistral Medium 3.1 measured
LiveCodeBench (Vals AI)Not measured44.8%Only Mistral Medium 3.1 measured
LiveCodeBench (Artificial Analysis run)31.7%40.6%Mistral Medium 3.1
SciCodeNot measured32.4%Only Mistral Medium 3.1 measured
Terminal-Bench Hard (Artificial Analysis run)8.3%10.6%Mistral Medium 3.1
ALE-BenchNot measured210.2Only Mistral Medium 3.1 measured
LMArena Text: coding12831433Mistral Medium 3.1
LMArena Text: software and IT12781436Mistral Medium 3.1
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
AA Intelligence Index7.79.2Mistral Medium 3.1
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
τ²-Bench Telecom28.9%40.6%Mistral Medium 3.1
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
MMMU Pro (Vals AI)64.0%63.0%GPT-4o (2024-08-06)
GPQA Diamond (Artificial Analysis run)52.1%58.8%Mistral Medium 3.1
Humanity's Last Exam (Artificial Analysis run)2.3%4.7%Mistral Medium 3.1
GPQA Diamond (Epoch AI run)49.2%Not measuredOnly GPT-4o (2024-08-06) measured
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts12641426Mistral Medium 3.1
LMArena Text: expert12411408Mistral Medium 3.1
LMArena Text: hard prompts in English12771435Mistral Medium 3.1
LMArena Vision: diagramsNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: homeworkNot measured1165Only Mistral Medium 3.1 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
AIME14.0%42.3%Mistral Medium 3.1
MATH 500 (Vals AI)75.2%87.0%Mistral Medium 3.1
MGSM (Vals AI)90.7%90.9%Mistral Medium 3.1
AA Math IndexNot measured38.3Only Mistral Medium 3.1 measured
AIME 2025 (Artificial Analysis run)Not measured38.3%Only Mistral Medium 3.1 measured
OTIS Mock AIME6.4%Not measuredOnly GPT-4o (2024-08-06) measured
FrontierMath, tiers 1 to 30.4%Not measuredOnly GPT-4o (2024-08-06) measured
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851408Mistral Medium 3.1
LMArena Text: mathematical work12831412Mistral Medium 3.1
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
MMLU Pro74.1%75.3%Mistral Medium 3.1
LegalBench (Vals AI)80.1%62.0%GPT-4o (2024-08-06)
CorpFin (Vals AI)39.4%50.7%Mistral Medium 3.1
TaxEval (Vals AI)71.1%70.3%GPT-4o (2024-08-06)
MortgageTax (Vals AI)61.0%36.5%GPT-4o (2024-08-06)
MedQA (Vals AI)88.2%78.2%GPT-4o (2024-08-06)
MMLU Pro (Artificial Analysis run)Not measured68.3%Only Mistral Medium 3.1 measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741444Mistral Medium 3.1
LMArena Text: medicine and healthcare12521449Mistral Medium 3.1
LMArena Text: legal and government12751439Mistral Medium 3.1
LMArena Text: business and finance12321418Mistral Medium 3.1
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
Design Arena: websitesNot measured1132Only Mistral Medium 3.1 measured
Design Arena: UI componentsNot measured1097Only Mistral Medium 3.1 measured
Design Arena: game devNot measured1073Only Mistral Medium 3.1 measured
Design Arena: data vizNot measured1144Only Mistral Medium 3.1 measured
Design Arena: 3DNot measured1081Only Mistral Medium 3.1 measured
Design Arena: all code categoriesNot measured1120Only Mistral Medium 3.1 measured
Design Arena: SVGNot measured992Only Mistral Medium 3.1 measured
Design Arena: ASCII artNot measured997Only Mistral Medium 3.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
IFBench36.0%39.8%Mistral Medium 3.1
Long context reasoning (AA-LCR)41.0%42.7%Mistral Medium 3.1
DTBenchNot measured67.5%Only Mistral Medium 3.1 measured
LMCANot measured19.1%Only Mistral Medium 3.1 measured
LMArena Text12831424Mistral Medium 3.1
LMArena Text: instruction following12671398Mistral Medium 3.1
LMArena Text: creative writing12741391Mistral Medium 3.1
LMArena Vision10651172Mistral Medium 3.1
LMArena Text: English12971438Mistral Medium 3.1
LMArena Text: non-English12621408Mistral Medium 3.1
LMArena Text: Chinese12541447Mistral Medium 3.1
LMArena Text: German12571432Mistral Medium 3.1
LMArena Text: Japanese12341379Mistral Medium 3.1
LMArena Text: Korean12181380Mistral Medium 3.1
LMArena Text: French12631460Mistral Medium 3.1
LMArena Text: Spanish12691433Mistral Medium 3.1
LMArena Text: Russian12721411Mistral Medium 3.1
LMArena Text: PolishNot measured1430Only Mistral Medium 3.1 measured
LMArena Text: multi-turn12791418Mistral Medium 3.1
LMArena Text: longer queries12831406Mistral Medium 3.1
LMArena Text: writing and language12901395Mistral Medium 3.1
LMArena Text: entertainment and media12671390Mistral Medium 3.1
LMArena Vision: English10721179Mistral Medium 3.1
LMArena Vision: Chinese10091190Mistral Medium 3.1
LMArena Vision: reading textNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: humorNot measured1168Only Mistral Medium 3.1 measured
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writingNot measured1200Only Mistral Medium 3.1 measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-08-06) and Mistral Medium 3.1
BenchmarkGPT-4o (2024-08-06)Mistral Medium 3.1Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4o (2024-08-06)$0.0075Fits in one request
Mistral Medium 3.1$0.0014Fits in one request

Mistral Medium 3.1 costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Mistral Medium 3.1$0.026Fits in one request

Mistral Medium 3.1 costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Mistral Medium 3.1$0.100Exceeds the context window

Mistral Medium 3.1 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4o (2024-08-06) and Mistral Medium 3.1
SpecificationOpenAI GPT-4o (2024-08-06)Mistral Medium 3.1
MakerOpenAIMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 6, 2024August 13, 2025
Context window128K131K
Input $/Mtok$2.50$0.40
Output $/Mtok$10.00$2.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Mistral Medium 3.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-08-06) for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Mistral Medium 3.1?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Mistral Medium 3.1?

Mistral Medium 3.1, with 131K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Mistral Medium 3.1 comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-08-06)'s coding index rests on 0 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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