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GPT-4 vs MiniMax M2.7

Updated October 21, 2026. 33 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4 Replace MiniMax M2.7
GPT-4OpenAI
Coding
22.4
Overall
6.7
Output $/M
$60.00
0
MiniMax M2.7MiniMax
Coding
28.4
Overall
22.8
Output $/M
$1.20
33
vs boards won of 33 shared

Decision reading

Pick MiniMax M2.7 for coding: it scores 28.4 on the Vibe Coding Index against 22.4 for GPT-4.

GPT-4 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Model AGPT-4OpenAI22.4/1001 independent benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax28.4/1001 independent benchmark$0.64 per solved task
Shared results
33
GPT-4 only
12
MiniMax M2.7 only
41
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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-4 22.4 from 1 benchmark, MiniMax M2.7 28.4 from 1 benchmark.
Widest category
Reasoning, 44.3 points apart on average over 4 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against MiniMax M2.7 87.4%, 52.5 points apart on a 0 to 100 scale.
Output price
GPT-4 $60.00 against MiniMax M2.7 $1.20 per million output tokens, 50.0x apart.
Context window
GPT-4 takes 8K 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-4BarsMiniMax M2.7Ties
Coding03
Intelligence02
Reasoning04
Math02
Knowledge04
General018
All shared boards033
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
SWE-bench Verified22.4%Not measuredOnly GPT-4 measured
SWE-bench Lite39.7%Not measuredOnly GPT-4 measured
SWE-bench (full test set)12.5%Not measuredOnly GPT-4 measured
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index13.152.6MiniMax 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
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: coding12091454MiniMax M2.7
LMArena Text: software and IT11951446MiniMax 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 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
AA Intelligence Index6.722.8MiniMax M2.7
Epoch Capabilities Index125.7145.8MiniMax M2.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax 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
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)34.9%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)Not measured29.6%Only MiniMax M2.7 measured
BIG-Bench Hard75.1%Not measuredOnly GPT-4 measured
LMArena Text: hard prompts12001422MiniMax M2.7
LMArena Text: expert11491443MiniMax M2.7
LMArena Text: hard prompts in English12091441MiniMax M2.7
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
GSM8K92.0%Not measuredOnly GPT-4 measured
LMArena Text: math12301421MiniMax M2.7
LMArena Text: mathematical work12281437MiniMax 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-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax 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)56.2%Not measuredOnly GPT-4 measured
MMLU86.4%Not measuredOnly GPT-4 measured
AI2 Reasoning Challenge96.3%Not measuredOnly GPT-4 measured
LMArena Text: science11771416MiniMax M2.7
LMArena Text: medicine and healthcare11451409MiniMax M2.7
LMArena Text: legal and government11911403MiniMax M2.7
LMArena Text: business and finance11451407MiniMax M2.7
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
Design Arena: websitesNot measured1243Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1210Only MiniMax M2.7 measured
Design Arena: game devNot measured1210Only MiniMax M2.7 measured
Design Arena: data vizNot measured1233Only MiniMax M2.7 measured
Design Arena: 3DNot measured1195Only 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-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench33.2%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)Not measured78.3%Only MiniMax M2.7 measured
DTBench62.7%Not measuredOnly GPT-4 measured
LMCA17.1%Not measuredOnly GPT-4 measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
HellaSwag95.3%Not measuredOnly GPT-4 measured
WinoGrande87.5%Not measuredOnly GPT-4 measured
LMArena Text12061405MiniMax M2.7
LMArena Text: instruction following12011405MiniMax M2.7
LMArena Text: creative writing11911354MiniMax M2.7
LMArena Text: English12201426MiniMax M2.7
LMArena Text: non-English11941382MiniMax M2.7
LMArena Text: Chinese11841442MiniMax M2.7
LMArena Text: German11981398MiniMax M2.7
LMArena Text: Japanese11371260MiniMax M2.7
LMArena Text: Korean10871314MiniMax M2.7
LMArena Text: French12201424MiniMax M2.7
LMArena Text: Spanish11971402MiniMax M2.7
LMArena Text: Russian11951383MiniMax M2.7
LMArena Text: Polish12651387MiniMax M2.7
LMArena Text: multi-turn12061412MiniMax M2.7
LMArena Text: longer queries11901418MiniMax M2.7
LMArena Text: writing and language12191376MiniMax M2.7
LMArena Text: entertainment and media11841360MiniMax M2.7
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4 and MiniMax M2.7
BenchmarkGPT-4MiniMax 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-4$0.060Fits in one request
MiniMax M2.7$0.0009Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

GPT-4$1.68Exceeds the context window
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

GPT-4$6.60Exceeds 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-4 and MiniMax M2.7
SpecificationOpenAI GPT-4MiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023March 18, 2026
Context window8K205K
Input $/Mtok$30.00$0.30
Output $/Mtok$60.00$1.20
Coding index22.428.4
Independent measurements behind it11
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4 better than MiniMax M2.7 for coding?

Pick MiniMax M2.7 for coding: it scores 28.4 on the Vibe Coding Index against 22.4 for GPT-4. GPT-4 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Which is cheaper, GPT-4 or MiniMax M2.7?

GPT-4 costs $30.00 per million input tokens and $60.00 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-4 or MiniMax M2.7?

MiniMax M2.7, with 205K tokens against 8K.

How many benchmarks is the GPT-4 vs MiniMax M2.7 comparison based on?

33 benchmarks have a result for both models as of October 21, 2026. GPT-4's coding index rests on 1 independent benchmark and MiniMax M2.7's on 1. Where only one model has a result, the page says so instead of naming a winner.

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