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GPT-4 Turbo vs MiniMax M2.7

MiniMax M2.7 leads GPT-4 Turbo on the Artificial Analysis Intelligence Index, 22.8 to 7.0, and MiniMax M2.7 costs 25.0x less per output token ($1.20 vs $30.00 per million). Updated October 22, 2026; 33 benchmarks have a result for both.

Replace GPT-4 Turbo Replace MiniMax M2.7
GPT-4 TurboOpenAI
Coding
No data
Overall
No data
Output $/M
$30.00
0
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
33
vs boards won of 33 shared

GPT-4 Turbo or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4 Turbo has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGPT-4 TurboOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
33
GPT-4 Turbo only
9
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-4 Turbo not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Coding, 30.6 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: expert: GPT-4 Turbo 1223 against MiniMax M2.7 1443, 36.6 points apart on a 0 to 100 scale.
Output price
GPT-4 Turbo $30.00 against MiniMax M2.7 $1.20 per million output tokens, 25.0x apart.
Context window
GPT-4 Turbo 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-4 TurboBarsMiniMax M2.7Ties
Coding03
Intelligence02
Reasoning04
Math02
Knowledge05
General017
All shared boards033
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index21.552.6MiniMax 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)29.1%Not measuredOnly GPT-4 Turbo 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: coding12681454MiniMax M2.7
LMArena Text: software and IT12671446MiniMax 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 index02
Intelligence benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax M2.7Leader
AA Intelligence Index7.022.8MiniMax M2.7
Epoch Capabilities Index127.3145.8MiniMax M2.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax 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 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)Not measured87.4%Only MiniMax M2.7 measured
Humanity's Last Exam (Artificial Analysis run)3.1%29.6%MiniMax M2.7
LMArena Text: hard prompts12511422MiniMax M2.7
LMArena Text: expert12231443MiniMax M2.7
LMArena Text: hard prompts in English12661441MiniMax M2.7
Neither model has a result on the other 17 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
LMArena Text: math12721421MiniMax M2.7
LMArena Text: mathematical work12681437MiniMax M2.7
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax M2.7Leader
MMLU ProNot measured80.4%Only MiniMax M2.7 measured
LegalBench (Vals AI)80.5%84.0%MiniMax M2.7
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
MedQA (Vals AI)82.0%Not measuredOnly GPT-4 Turbo measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)69.4%Not measuredOnly GPT-4 Turbo measured
MMLU81.3%Not measuredOnly GPT-4 Turbo measured
LMArena Text: science12561416MiniMax M2.7
LMArena Text: medicine and healthcare12391409MiniMax M2.7
LMArena Text: legal and government12651403MiniMax M2.7
LMArena Text: business and finance12221407MiniMax M2.7
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-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax 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-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax 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
DTBench61.6%Not measuredOnly GPT-4 Turbo measured
LMCA9.8%Not measuredOnly GPT-4 Turbo measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text12721405MiniMax M2.7
LMArena Text: instruction following12491405MiniMax M2.7
LMArena Text: creative writing12681354MiniMax M2.7
LMArena Vision1090Not measuredOnly GPT-4 Turbo measured
LMArena Text: English12911426MiniMax M2.7
LMArena Text: non-English12451382MiniMax M2.7
LMArena Text: Chinese12421442MiniMax M2.7
LMArena Text: German12591398MiniMax M2.7
LMArena Text: Japanese11941260MiniMax M2.7
LMArena Text: Korean11871314MiniMax M2.7
LMArena Text: French12761424MiniMax M2.7
LMArena Text: Spanish12601402MiniMax M2.7
LMArena Text: Russian12591383MiniMax M2.7
LMArena Text: Polish13021387MiniMax M2.7
LMArena Text: multi-turn12671412MiniMax M2.7
LMArena Text: longer queries12541418MiniMax M2.7
LMArena Text: writing and language12791376MiniMax M2.7
LMArena Text: entertainment and media12581360MiniMax M2.7
LMArena Vision: English1097Not measuredOnly GPT-4 Turbo measured
LMArena Vision: Chinese1045Not measuredOnly GPT-4 Turbo measured
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4 Turbo and MiniMax M2.7
BenchmarkGPT-4 TurboMiniMax 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 Turbo$0.025Fits 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 Turbo$0.590Fits in one request
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

GPT-4 Turbo$2.30Exceeds 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 Turbo and MiniMax M2.7
SpecificationOpenAI GPT-4 TurboMiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedApril 9, 2024March 18, 2026
Context window128K205K
Input $/Mtok$10.00$0.30
Output $/Mtok$30.00$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

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

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

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

GPT-4 Turbo costs $10.00 per million input tokens and $30.00 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 Turbo or MiniMax M2.7?

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

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

33 benchmarks have a result for both models as of October 22, 2026. GPT-4 Turbo'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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