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GPT-5 Mini vs MiniMax M2.7

MiniMax M2.7 leads GPT-5 Mini on coding, 36.2 to 34.2 on the Vibe Coding Index, and MiniMax M2.7 costs 1.7x less per output token ($1.20 vs $2.00 per million). Updated October 22, 2026; 60 benchmarks have a result for both.

Replace GPT-5 Mini Replace MiniMax M2.7
GPT-5 MiniOpenAI
Coding
34.2
Overall
43.7
Output $/M
$2.00
11
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
49
vs boards won of 60 shared

GPT-5 Mini or MiniMax M2.7: which to pick

Pick MiniMax M2.7 for coding: it scores 36.2 on the Vibe Coding Index against 34.2 for GPT-5 Mini.

Pick GPT-5 Mini for a lower cost per solved task ($0.08 against $0.64) and a larger context window (400K against 205K tokens).

Model AGPT-5 MiniOpenAI34.2/1002 independent benchmarks$0.08 per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
60
GPT-5 Mini only
41
MiniMax M2.7 only
14
Like-for-like categories
8 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-5 Mini 34.2 from 2 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 13.7 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: 3D: GPT-5 Mini 1036 against MiniMax M2.7 1195, 19.9 points apart on a 0 to 100 scale.
Output price
GPT-5 Mini $2.00 against MiniMax M2.7 $1.20 per million output tokens, 1.7x apart.
Context window
GPT-5 Mini takes 400K 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-5 MiniBarsMiniMax M2.7Ties
Coding36
Intelligence02
Agentic work01
Reasoning06
Math22
Knowledge46
Web design08
General218
All shared boards1149
CodingLike-for-likeSix of these boards feed the Vibe Coding Index36
Coding benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
SWE-bench Verified59.8%Not measuredOnly GPT-5 Mini measured
SWE-bench Multilingual39.7%Not measuredOnly GPT-5 Mini measured
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
LiveCodeBench (Vals AI)86.6%79.9%GPT-5 Mini
SWE-bench Verified (Vals AI)60.8%73.8%MiniMax M2.7
Vibe Code Bench (Vals AI)14.2%11.9%GPT-5 Mini
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)83.8%Not measuredOnly GPT-5 Mini measured
SciCode39.0%50.1%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)33.3%39.4%MiniMax M2.7
Terminal-Bench34.8%45.1%MiniMax M2.7
WeirdML52.7%37.0%GPT-5 Mini
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: coding14061454MiniMax M2.7
LMArena Text: software and IT13941446MiniMax 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 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
AA Intelligence Index20.622.8MiniMax M2.7
Epoch Capabilities Index145.3145.8MiniMax M2.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax 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 Telecom71.1%84.8%MiniMax M2.7
Vending-Bench 2-$31Not measuredOnly GPT-5 Mini measured
Berkeley Function Calling Leaderboard (BFCL)55.5%Not measuredOnly GPT-5 Mini measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
GPQA Diamond80.3%86.6%MiniMax M2.7
MMMU Pro (Vals AI)78.9%Not measuredOnly GPT-5 Mini measured
GPQA Diamond (Artificial Analysis run)82.8%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)21.5%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)75.0%Not measuredOnly GPT-5 Mini measured
ARC-AGI54.3%Not measuredOnly GPT-5 Mini measured
ARC-AGI-24.4%Not measuredOnly GPT-5 Mini measured
Chess puzzles24.0%Not measuredOnly GPT-5 Mini measured
Mystery game puzzles10.0%Not measuredOnly GPT-5 Mini measured
VPCT40.2%Not measuredOnly GPT-5 Mini measured
LMArena Text: hard prompts13801422MiniMax M2.7
LMArena Text: expert13791443MiniMax M2.7
LMArena Text: hard prompts in English13831441MiniMax M2.7
LMArena Vision: diagrams1202Not measuredOnly GPT-5 Mini measured
LMArena Vision: homework1210Not measuredOnly GPT-5 Mini measured
Kagi LLM Benchmark70.3%Not measuredOnly GPT-5 Mini measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index22
Math benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
AIME91.5%91.0%GPT-5 Mini
MATH 500 (Vals AI)94.8%Not measuredOnly GPT-5 Mini measured
MGSM (Vals AI)92.6%Not measuredOnly GPT-5 Mini measured
AA Math Index90.7Not measuredOnly GPT-5 Mini measured
AIME 2025 (Artificial Analysis run)90.7%Not measuredOnly GPT-5 Mini measured
OTIS Mock AIME86.7%Not measuredOnly GPT-5 Mini measured
FrontierMath, tiers 1 to 346.7%Not measuredOnly GPT-5 Mini measured
FrontierMath, tier 412.2%Not measuredOnly GPT-5 Mini measured
ProofBench9.0%3.0%GPT-5 Mini
MATH level 597.8%Not measuredOnly GPT-5 Mini measured
LMArena Text: math13781421MiniMax M2.7
LMArena Text: mathematical work13801437MiniMax M2.7
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
MMLU Pro82.2%80.4%GPT-5 Mini
LegalBench (Vals AI)81.8%84.0%MiniMax M2.7
CaseLaw (Vals AI)68.5%60.9%GPT-5 Mini
CorpFin (Vals AI)60.2%61.2%MiniMax M2.7
TaxEval (Vals AI)75.2%66.6%GPT-5 Mini
MortgageTax (Vals AI)66.9%Not measuredOnly GPT-5 Mini measured
MedQA (Vals AI)96.1%Not measuredOnly GPT-5 Mini measured
MedCode (Vals AI)43.0%34.4%GPT-5 Mini
MMLU Pro (Artificial Analysis run)83.7%Not measuredOnly GPT-5 Mini measured
SimpleQA Verified21.6%Not measuredOnly GPT-5 Mini measured
LMArena Text: science13711416MiniMax M2.7
LMArena Text: medicine and healthcare13801409MiniMax M2.7
LMArena Text: legal and government13751403MiniMax M2.7
LMArena Text: business and finance13581407MiniMax M2.7
FACTS Benchmark Suite38.6%Not measuredOnly GPT-5 Mini measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
Design Arena: websites11251243MiniMax M2.7
Design Arena: UI components11031210MiniMax M2.7
Design Arena: game dev11291209MiniMax M2.7
Design Arena: data viz11311233MiniMax M2.7
Design Arena: 3D10361195MiniMax M2.7
Design Arena: all code categories11141233MiniMax M2.7
Design Arena: SVG10911137MiniMax M2.7
Design Arena: ASCII art11251136MiniMax M2.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index218
General benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax M2.7Leader
SAGE (Vals AI)43.0%Not measuredOnly GPT-5 Mini measured
MedScribe (Vals AI)80.6%79.9%GPT-5 Mini
IFBench75.4%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)72.3%78.3%MiniMax M2.7
DTBench80.5%Not measuredOnly GPT-5 Mini measured
LMCA34.2%Not measuredOnly GPT-5 Mini measured
Fiction.LiveBench69.4%Not measuredOnly GPT-5 Mini measured
Creative writing (Lech Mazur)83.1%Not measuredOnly GPT-5 Mini measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text13731405MiniMax M2.7
LMArena Text: instruction following13571405MiniMax M2.7
LMArena Text: creative writing13251354MiniMax M2.7
LMArena Vision1202Not measuredOnly GPT-5 Mini measured
LMArena Text: English13791426MiniMax M2.7
LMArena Text: non-English13631382MiniMax M2.7
LMArena Text: Chinese13861442MiniMax M2.7
LMArena Text: German13661398MiniMax M2.7
LMArena Text: Japanese13411260GPT-5 Mini
LMArena Text: Korean13081314MiniMax M2.7
LMArena Text: French13861424MiniMax M2.7
LMArena Text: Spanish13541402MiniMax M2.7
LMArena Text: Russian13621383MiniMax M2.7
LMArena Text: Polish13781387MiniMax M2.7
LMArena Text: multi-turn13631412MiniMax M2.7
LMArena Text: longer queries13551418MiniMax M2.7
LMArena Text: writing and language13441376MiniMax M2.7
LMArena Text: entertainment and media13401360MiniMax M2.7
LMArena Vision: English1216Not measuredOnly GPT-5 Mini measured
LMArena Vision: Chinese1214Not measuredOnly GPT-5 Mini measured
LMArena Vision: reading text1199Not measuredOnly GPT-5 Mini measured
LMArena Vision: humor1205Not measuredOnly GPT-5 Mini measured
LMArena Vision: captioning1217Not measuredOnly GPT-5 Mini measured
LMArena Vision: recognizing things1226Not measuredOnly GPT-5 Mini measured
LMArena Vision: creative writing1181Not measuredOnly GPT-5 Mini measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5 Mini and MiniMax M2.7
BenchmarkGPT-5 MiniMiniMax 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-5 Mini$0.0013Fits in one request
MiniMax M2.7$0.0009Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

GPT-5 Mini$0.018Fits in one request
MiniMax M2.7$0.019Fits in one request

GPT-5 Mini costs less

Agent loop

200K input + 10K output tokens

GPT-5 Mini$0.070Fits in one request
MiniMax M2.7$0.072Exceeds the context window

GPT-5 Mini 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-5 Mini and MiniMax M2.7
SpecificationOpenAI GPT-5 MiniMiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedAugust 7, 2025March 18, 2026
Context window400K205K
Input $/Mtok$0.25$0.30
Output $/Mtok$2.00$1.20
Coding index34.236.2
Independent measurements behind it23
Cost per solved task$0.08$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 Mini better than MiniMax M2.7 for coding?

Pick MiniMax M2.7 for coding: it scores 36.2 on the Vibe Coding Index against 34.2 for GPT-5 Mini. Pick GPT-5 Mini for a lower cost per solved task ($0.08 against $0.64) and a larger context window (400K against 205K tokens).

Which is cheaper, GPT-5 Mini or MiniMax M2.7?

GPT-5 Mini costs $0.25 per million input tokens and $2.00 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GPT-5 Mini costs $0.08 and MiniMax M2.7 $0.64.

Which has the bigger context window, GPT-5 Mini or MiniMax M2.7?

GPT-5 Mini, with 400K tokens against 205K.

How many benchmarks is the GPT-5 Mini vs MiniMax M2.7 comparison based on?

60 benchmarks have a result for both models as of October 22, 2026. GPT-5 Mini's coding index rests on 2 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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