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MiniMax M2.7 vs Mistral Large

MiniMax M2.7 leads Mistral Large on the Artificial Analysis Intelligence Index, 22.8 to 5.8, and MiniMax M2.7 costs 5.0x less per output token ($1.20 vs $6.00 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace MiniMax M2.7 Replace Mistral Large
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
37
Mistral LargeMistral AI
Coding
No data
Overall
No data
Output $/M
$6.00
0
vs boards won of 37 shared

MiniMax M2.7 or Mistral Large: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved taskModel BMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
37
MiniMax M2.7 only
37
Mistral Large only
7
Like-for-like categories
6 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
MiniMax M2.7 36.2 from 3 benchmarks, Mistral Large not benchmarked.
Widest category
Math, 46.2 points apart on average over 3 shared benchmarks.
Widest single gap
AIME: MiniMax M2.7 91.0% against Mistral Large 9.2%, 81.9 points apart on a 0 to 100 scale.
Output price
MiniMax M2.7 $1.20 against Mistral Large $6.00 per million output tokens, 5.0x apart.
Context window
MiniMax M2.7 takes 205K tokens, Mistral Large 128K.

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
CategoryMiniMax M2.7BarsMistral LargeTies
Coding30
Intelligence20
Reasoning60
Math30
Knowledge60
General170
All shared boards370
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
LMArena WebDev1399Not measuredOnly MiniMax M2.7 measured
AA Coding Index52.6Not measuredOnly MiniMax M2.7 measured
LiveCodeBench (Vals AI)79.9%37.1%MiniMax M2.7
SWE-bench Verified (Vals AI)73.8%Not measuredOnly MiniMax M2.7 measured
Vibe Code Bench (Vals AI)11.9%Not measuredOnly MiniMax M2.7 measured
Code migration (Vals AI)8.7%Not measuredOnly MiniMax M2.7 measured
ProgramBench (Vals AI)0.0%Not measuredOnly MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)48.7%Not measuredOnly MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)Not measured17.8%Only Mistral Large measured
SciCode50.1%Not measuredOnly MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)39.4%Not measuredOnly MiniMax M2.7 measured
Terminal-Bench45.1%Not measuredOnly MiniMax M2.7 measured
WeirdML37.0%Not measuredOnly MiniMax M2.7 measured
ALE-Bench599.3Not measuredOnly MiniMax M2.7 measured
LMArena Text: coding14541275MiniMax M2.7
LMArena Text: software and IT14461274MiniMax M2.7
LMArena WebDev: React1388Not measuredOnly MiniMax M2.7 measured
LMArena WebDev: HTML1401Not measuredOnly MiniMax M2.7 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
AA Intelligence Index22.85.8MiniMax M2.7
Epoch Capabilities Index145.8122.1MiniMax M2.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
AA Agentic Index15.3Not measuredOnly MiniMax M2.7 measured
Finance Agent (Vals AI)27.9%Not measuredOnly MiniMax M2.7 measured
Tax Agent Bench (Vals AI)6.9%Not measuredOnly MiniMax M2.7 measured
Legal Research Bench (Vals AI)10.6%Not measuredOnly MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)28.4%Not measuredOnly MiniMax M2.7 measured
τ²-Bench Telecom84.8%Not measuredOnly MiniMax M2.7 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
GPQA Diamond86.6%47.7%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)87.4%35.1%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)29.6%3.5%MiniMax M2.7
LMArena Text: hard prompts14221257MiniMax M2.7
LMArena Text: expert14431208MiniMax M2.7
LMArena Text: hard prompts in English14411268MiniMax M2.7
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
AIME91.0%9.2%MiniMax M2.7
MATH 500 (Vals AI)Not measured74.4%Only Mistral Large measured
MGSM (Vals AI)Not measured87.2%Only Mistral Large measured
ProofBench3.0%Not measuredOnly MiniMax M2.7 measured
LMArena Text: math14211261MiniMax M2.7
LMArena Text: mathematical work14371257MiniMax M2.7
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
Knowledge benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
MMLU Pro80.4%69.7%MiniMax M2.7
LegalBench (Vals AI)84.0%Not measuredOnly MiniMax M2.7 measured
CaseLaw (Vals AI)60.9%Not measuredOnly MiniMax M2.7 measured
CorpFin (Vals AI)61.2%Not measuredOnly MiniMax M2.7 measured
TaxEval (Vals AI)66.6%63.8%MiniMax M2.7
MedQA (Vals AI)Not measured76.2%Only Mistral Large measured
MedCode (Vals AI)34.4%Not measuredOnly MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)Not measured51.5%Only Mistral Large measured
MMLUNot measured68.8%Only Mistral Large measured
LMArena Text: science14161260MiniMax M2.7
LMArena Text: medicine and healthcare14091255MiniMax M2.7
LMArena Text: legal and government14031275MiniMax M2.7
LMArena Text: business and finance14071238MiniMax M2.7
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
Design Arena: websites1243Not measuredOnly MiniMax M2.7 measured
Design Arena: UI components1210Not measuredOnly MiniMax M2.7 measured
Design Arena: game dev1209Not measuredOnly MiniMax M2.7 measured
Design Arena: data viz1233Not measuredOnly MiniMax M2.7 measured
Design Arena: 3D1195Not measuredOnly MiniMax M2.7 measured
Design Arena: all code categories1233Not measuredOnly MiniMax M2.7 measured
Design Arena: SVG1137Not measuredOnly MiniMax M2.7 measured
Design Arena: ASCII art1136Not measuredOnly MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
MedScribe (Vals AI)79.9%Not measuredOnly MiniMax M2.7 measured
IFBench75.7%Not measuredOnly MiniMax M2.7 measured
Long context reasoning (AA-LCR)78.3%Not measuredOnly MiniMax M2.7 measured
DTBenchNot measured60.8%Only Mistral Large measured
ExploitBench13.3%Not measuredOnly MiniMax M2.7 measured
LMArena Text14051265MiniMax M2.7
LMArena Text: instruction following14051249MiniMax M2.7
LMArena Text: creative writing13541242MiniMax M2.7
LMArena Text: English14261284MiniMax M2.7
LMArena Text: non-English13821235MiniMax M2.7
LMArena Text: Chinese14421239MiniMax M2.7
LMArena Text: German13981242MiniMax M2.7
LMArena Text: Japanese12601170MiniMax M2.7
LMArena Text: Korean13141202MiniMax M2.7
LMArena Text: French14241325MiniMax M2.7
LMArena Text: Spanish14021268MiniMax M2.7
LMArena Text: Russian13831252MiniMax M2.7
LMArena Text: Polish13871157MiniMax M2.7
LMArena Text: multi-turn14121260MiniMax M2.7
LMArena Text: longer queries14181261MiniMax M2.7
LMArena Text: writing and language13761261MiniMax M2.7
LMArena Text: entertainment and media13601230MiniMax M2.7
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for MiniMax M2.7 and Mistral Large
BenchmarkMiniMax M2.7Mistral LargeLeader
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

MiniMax M2.7$0.0009Fits in one request
Mistral Large$0.0050Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

MiniMax M2.7$0.019Fits in one request
Mistral Large$0.118Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

MiniMax M2.7$0.072Exceeds the context window
Mistral Large$0.460Exceeds the context window

MiniMax M2.7 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 MiniMax M2.7 and Mistral Large
SpecificationMiniMax M2.7Mistral Large
MakerMiniMaxMistral AI
WeightsOpen weightsNo public weights listed
ReleasedMarch 18, 2026February 26, 2024
Context window205K128K
Input $/Mtok$0.30$2.00
Output $/Mtok$1.20$6.00
Coding index36.2Not benchmarked
Independent measurements behind it30
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is MiniMax M2.7 better than Mistral Large for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, MiniMax M2.7 or Mistral Large?

MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, MiniMax M2.7 or Mistral Large?

MiniMax M2.7, with 205K tokens against 128K.

How many benchmarks is the MiniMax M2.7 vs Mistral Large comparison based on?

37 benchmarks have a result for both models as of October 22, 2026. MiniMax M2.7's coding index rests on 3 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.

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