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GPT-4o (2024-05-13) vs MiniMax M2.7

MiniMax M2.7 leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 22.8 to 7.3, and MiniMax M2.7 costs 12.5x less per output token ($1.20 vs $15.00 per million). Updated October 22, 2026; 33 benchmarks have a result for both.

Replace GPT-4o (2024-05-13) Replace MiniMax M2.7
GPT-4o (2024-05-13)OpenAI
Coding
No data
Overall
27.1
Output $/M
$15.00
0
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
33
vs boards won of 33 shared

GPT-4o (2024-05-13) or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGPT-4o (2024-05-13)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
33
GPT-4o (2024-05-13) only
11
MiniMax M2.7 only
41
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
GPT-4o (2024-05-13) not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Reasoning, 29.9 points apart on average over 6 shared benchmarks.
Widest single gap
GPQA Diamond: GPT-4o (2024-05-13) 50.2% against MiniMax M2.7 86.6%, 36.4 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-05-13) $15.00 against MiniMax M2.7 $1.20 per million output tokens, 12.5x apart.
Context window
GPT-4o (2024-05-13) 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 (2024-05-13)BarsMiniMax M2.7Ties
Coding03
Intelligence01
Reasoning06
Math02
Knowledge04
General017
All shared boards033
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index24.252.6MiniMax M2.7
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)33.4%Not measuredOnly GPT-4o (2024-05-13) measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured39.4%Only MiniMax M2.7 measured
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: coding12971454MiniMax M2.7
LMArena Text: software and IT12971446MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1401Only MiniMax M2.7 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
AA Intelligence Index7.322.8MiniMax M2.7
Epoch Capabilities IndexNot measured145.8Only MiniMax M2.7 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Agentic work benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)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 TelecomNot measured84.8%Only MiniMax M2.7 measured
METR time horizons40.8%Not measuredOnly GPT-4o (2024-05-13) measured
BALROG32.3%Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
GPQA Diamond50.2%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)52.6%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)1.8%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)48.9%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: hard prompts12811422MiniMax M2.7
LMArena Text: expert12501443MiniMax M2.7
LMArena Text: hard prompts in English12931441MiniMax M2.7
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
OTIS Mock AIME6.3%Not measuredOnly GPT-4o (2024-05-13) measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
MATH level 551.0%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: math12841421MiniMax M2.7
LMArena Text: mathematical work12841437MiniMax M2.7
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
MMLU ProNot measured80.4%Only MiniMax M2.7 measured
LegalBench (Vals AI)Not measured84.0%Only MiniMax M2.7 measured
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)Not measured61.2%Only MiniMax M2.7 measured
TaxEval (Vals AI)Not measured66.6%Only MiniMax M2.7 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)74.0%Not measuredOnly GPT-4o (2024-05-13) measured
MMLU84.2%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: science12951416MiniMax M2.7
LMArena Text: medicine and healthcare12781409MiniMax M2.7
LMArena Text: legal and government13141403MiniMax M2.7
LMArena Text: business and finance12621407MiniMax M2.7
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
Design Arena: websitesNot measured1242Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1207Only 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 measured1137Only 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 index017
General benchmarks for GPT-4o (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)MiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBenchNot measured75.7%Only MiniMax M2.7 measured
Long context reasoning (AA-LCR)Not measured78.3%Only MiniMax M2.7 measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text13001405MiniMax M2.7
LMArena Text: instruction following12781405MiniMax M2.7
LMArena Text: creative writing12911354MiniMax M2.7
LMArena Vision1137Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: English13121426MiniMax M2.7
LMArena Text: non-English12831382MiniMax M2.7
LMArena Text: Chinese12771442MiniMax M2.7
LMArena Text: German12831398MiniMax M2.7
LMArena Text: Japanese12571260MiniMax M2.7
LMArena Text: Korean12341314MiniMax M2.7
LMArena Text: French13041424MiniMax M2.7
LMArena Text: Spanish12921402MiniMax M2.7
LMArena Text: Russian12851383MiniMax M2.7
LMArena Text: Polish13651387MiniMax M2.7
LMArena Text: multi-turn13021412MiniMax M2.7
LMArena Text: longer queries12881418MiniMax M2.7
LMArena Text: writing and language12981376MiniMax M2.7
LMArena Text: entertainment and media12891360MiniMax M2.7
LMArena Vision: English1147Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Vision: Chinese1132Not measuredOnly GPT-4o (2024-05-13) 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 (2024-05-13) and MiniMax M2.7
BenchmarkGPT-4o (2024-05-13)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 (2024-05-13)$0.013Fits 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 (2024-05-13)$0.295Fits 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 (2024-05-13)$1.15Exceeds 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 (2024-05-13) and MiniMax M2.7
SpecificationOpenAI GPT-4o (2024-05-13)MiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedMay 13, 2024March 18, 2026
Context window128K205K
Input $/Mtok$5.00$0.30
Output $/Mtok$15.00$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-05-13) better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GPT-4o (2024-05-13) or MiniMax M2.7?

GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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 (2024-05-13) or MiniMax M2.7?

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

How many benchmarks is the GPT-4o (2024-05-13) vs MiniMax M2.7 comparison based on?

33 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'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.

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