Enter opens the highlighted result. Esc closes.

GPT-4o (2024-08-06) vs MiniMax M2.7

MiniMax M2.7 leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 22.8 to 7.7, and MiniMax M2.7 costs 8.3x less per output token ($1.20 vs $10.00 per million). Updated October 23, 2026; 39 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace MiniMax M2.7
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
1
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
38
vs boards won of 39 shared

GPT-4o (2024-08-06) or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o (2024-08-06) has none, so the two cannot be ranked on coding.

Pick GPT-4o (2024-08-06) for work measured by TaxEval (Vals AI), where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
39
GPT-4o (2024-08-06) only
19
MiniMax M2.7 only
35
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, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 55.8 points apart on average over 1 shared benchmark.
Widest single gap
AIME: GPT-4o (2024-08-06) 14.0% against MiniMax M2.7 91.0%, 77.1 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against MiniMax M2.7 $1.20 per million output tokens, 8.3x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, MiniMax M2.7 205K.

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)BarsMiniMax M2.7Ties
Coding03
Intelligence01
Agentic work01
Reasoning05
Math03
Knowledge17
General018
All shared boards138
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena WebDevNot measured1398Only MiniMax M2.7 measured
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
LiveCodeBench (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)Not measured11.9%Only MiniMax M2.7 measured
Code migration (Vals AI)Not measured8.7%Only MiniMax M2.7 measured
ProgramBench (Vals AI)Not measured0.0%Only MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured48.7%Only MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly GPT-4o (2024-08-06) measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)8.3%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: coding12831454MiniMax M2.7
LMArena Text: software and IT12781445MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1402Only MiniMax M2.7 measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
AA Intelligence Index7.722.8MiniMax M2.7
Epoch Capabilities IndexNot measured145.8Only MiniMax M2.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
Finance Agent (Vals AI)Not measured27.9%Only MiniMax M2.7 measured
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)Not measured10.6%Only MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)Not measured28.4%Only MiniMax M2.7 measured
τ²-Bench Telecom28.9%84.8%MiniMax M2.7
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)2.3%29.6%MiniMax M2.7
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 prompts12641423MiniMax M2.7
LMArena Text: expert12411443MiniMax M2.7
LMArena Text: hard prompts in English12771441MiniMax M2.7
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
AIME14.0%91.0%MiniMax M2.7
MATH 500 (Vals AI)75.2%Not measuredOnly GPT-4o (2024-08-06) measured
MGSM (Vals AI)90.7%Not measuredOnly GPT-4o (2024-08-06) 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
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851421MiniMax M2.7
LMArena Text: mathematical work12831437MiniMax M2.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index17
Knowledge benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
MMLU Pro74.1%80.4%MiniMax M2.7
LegalBench (Vals AI)80.1%84.0%MiniMax M2.7
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)39.4%61.2%MiniMax M2.7
TaxEval (Vals AI)71.1%66.6%GPT-4o (2024-08-06)
MortgageTax (Vals AI)61.0%Not measuredOnly GPT-4o (2024-08-06) measured
MedQA (Vals AI)88.2%Not measuredOnly GPT-4o (2024-08-06) measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741416MiniMax M2.7
LMArena Text: medicine and healthcare12521409MiniMax M2.7
LMArena Text: legal and government12751403MiniMax M2.7
LMArena Text: business and finance12321407MiniMax M2.7
Neither model has a result on the other 6 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 MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
Design Arena: websitesNot measured1242Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1208Only MiniMax M2.7 measured
Design Arena: game devNot measured1209Only MiniMax M2.7 measured
Design Arena: data vizNot measured1233Only MiniMax M2.7 measured
Design Arena: 3DNot measured1194Only MiniMax M2.7 measured
Design Arena: all code categoriesNot measured1233Only MiniMax M2.7 measured
Design Arena: SVGNot measured1136Only MiniMax M2.7 measured
Design Arena: ASCII artNot measured1136Only 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 index018
General benchmarks for GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench36.0%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)41.0%78.3%MiniMax M2.7
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text12831405MiniMax M2.7
LMArena Text: instruction following12671405MiniMax M2.7
LMArena Text: creative writing12741354MiniMax M2.7
LMArena Vision1065Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: English12971426MiniMax M2.7
LMArena Text: non-English12621383MiniMax M2.7
LMArena Text: Chinese12541441MiniMax M2.7
LMArena Text: German12571398MiniMax M2.7
LMArena Text: Japanese12341259MiniMax M2.7
LMArena Text: Korean12181316MiniMax M2.7
LMArena Text: French12631424MiniMax M2.7
LMArena Text: Spanish12691403MiniMax M2.7
LMArena Text: Russian12721384MiniMax M2.7
LMArena Text: PolishNot measured1388Only MiniMax M2.7 measured
LMArena Text: multi-turn12791412MiniMax M2.7
LMArena Text: longer queries12831419MiniMax M2.7
LMArena Text: writing and language12901376MiniMax M2.7
LMArena Text: entertainment and media12671361MiniMax M2.7
LMArena Vision: English1072Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Vision: Chinese1009Not measuredOnly GPT-4o (2024-08-06) measured
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 GPT-4o (2024-08-06) and MiniMax M2.7
BenchmarkGPT-4o (2024-08-06)MiniMax M2.7Leader
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
MiniMax M2.7$0.0009Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
MiniMax M2.7$0.072Exceeds 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 GPT-4o (2024-08-06) and MiniMax M2.7
SpecificationOpenAI GPT-4o (2024-08-06)MiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024March 18, 2026
Context window128K205K
Input $/Mtok$2.50$0.30
Output $/Mtok$10.00$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o (2024-08-06) has none, so the two cannot be ranked on coding. Pick GPT-4o (2024-08-06) for work measured by TaxEval (Vals AI), where it leads.

Which is cheaper, GPT-4o (2024-08-06) or MiniMax M2.7?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, GPT-4o (2024-08-06) or MiniMax M2.7?

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

How many benchmarks is the GPT-4o (2024-08-06) vs MiniMax M2.7 comparison based on?

39 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-08-06)'s coding index rests on 0 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.

Compare next