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GPT-4o (2024-05-13) vs MiniMax M2.1

MiniMax M2.1 leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 20.9 to 7.3, and MiniMax M2.1 costs 12.5x less per output token ($1.20 vs $15.00 per million). Updated October 22, 2026; 6 benchmarks have a result for both.

Replace GPT-4o (2024-05-13) Replace MiniMax M2.1
GPT-4o (2024-05-13)OpenAI
Coding
No data
Overall
27.1
Output $/M
$15.00
0
MiniMax M2.1MiniMax
Coding
No data
Overall
No data
Output $/M
$1.20
6
vs boards won of 6 shared

GPT-4o (2024-05-13) or MiniMax M2.1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4o (2024-05-13) for work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.

Model AGPT-4o (2024-05-13)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.1MiniMaxNot benchmarked1 independent benchmark$0.59 per solved task
Shared results
6
GPT-4o (2024-05-13) only
38
MiniMax M2.1 only
28
Like-for-like categories
4 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 M2.1 not benchmarked.
Widest category
Coding, 47.6 points apart on average over 1 shared benchmark.
Widest single gap
LiveCodeBench (Artificial Analysis run): GPT-4o (2024-05-13) 33.4% against MiniMax M2.1 81.0%, 47.6 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-05-13) $15.00 against MiniMax M2.1 $1.20 per million output tokens, 12.5x apart.
Context window
GPT-4o (2024-05-13) takes 128K tokens, MiniMax M2.1 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 M2.1Ties
Coding01
Intelligence01
Reasoning03
Knowledge01
All shared boards06
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
AA Coding Index24.2Not measuredOnly GPT-4o (2024-05-13) measured
LiveCodeBench (Vals AI)Not measured81.8%Only MiniMax M2.1 measured
SWE-bench Verified (Vals AI)Not measured74.8%Only MiniMax M2.1 measured
LiveCodeBench (Artificial Analysis run)33.4%81.0%MiniMax M2.1
Terminal-Bench Hard (Artificial Analysis run)Not measured28.8%Only MiniMax M2.1 measured
Terminal-BenchNot measured36.6%Only MiniMax M2.1 measured
ALE-BenchNot measured623.8Only MiniMax M2.1 measured
LMArena Text: coding1297Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: software and IT1297Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
AA Intelligence Index7.320.9MiniMax M2.1
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
τ²-Bench TelecomNot measured85.4%Only MiniMax M2.1 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 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
GPQA Diamond50.2%78.5%MiniMax M2.1
GPQA Diamond (Artificial Analysis run)52.6%83.0%MiniMax M2.1
Humanity's Last Exam (Artificial Analysis run)1.8%23.2%MiniMax M2.1
GPQA Diamond (Epoch AI run)48.9%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: hard prompts1281Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: expert1250Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: hard prompts in English1293Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Math benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
AIMENot measured77.9%Only MiniMax M2.1 measured
MATH 500 (Vals AI)Not measured89.0%Only MiniMax M2.1 measured
MGSM (Vals AI)Not measured87.8%Only MiniMax M2.1 measured
AA Math IndexNot measured82.7Only MiniMax M2.1 measured
AIME 2025 (Artificial Analysis run)Not measured82.7%Only MiniMax M2.1 measured
OTIS Mock AIME6.3%Not measuredOnly GPT-4o (2024-05-13) measured
MATH level 551.0%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: math1284Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: mathematical work1284Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
Knowledge benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
MMLU ProNot measured87.0%Only MiniMax M2.1 measured
LegalBench (Vals AI)Not measured75.5%Only MiniMax M2.1 measured
CaseLaw (Vals AI)Not measured55.8%Only MiniMax M2.1 measured
CorpFin (Vals AI)Not measured58.9%Only MiniMax M2.1 measured
TaxEval (Vals AI)Not measured66.3%Only MiniMax M2.1 measured
MedQA (Vals AI)Not measured91.2%Only MiniMax M2.1 measured
MedCode (Vals AI)Not measured34.1%Only MiniMax M2.1 measured
MMLU Pro (Artificial Analysis run)74.0%87.5%MiniMax M2.1
MMLU84.2%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: science1295Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: medicine and healthcare1278Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: legal and government1314Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: business and finance1262Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
Design Arena: websitesNot measured1201Only MiniMax M2.1 measured
Design Arena: UI componentsNot measured1220Only MiniMax M2.1 measured
Design Arena: game devNot measured1134Only MiniMax M2.1 measured
Design Arena: data vizNot measured1206Only MiniMax M2.1 measured
Design Arena: 3DNot measured1164Only MiniMax M2.1 measured
Design Arena: all code categoriesNot measured1190Only MiniMax M2.1 measured
Design Arena: SVGNot measured1131Only MiniMax M2.1 measured
Neither model has a result on the other 2 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
General benchmarks for GPT-4o (2024-05-13) and MiniMax M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
MedScribe (Vals AI)Not measured80.8%Only MiniMax M2.1 measured
IFBenchNot measured69.9%Only MiniMax M2.1 measured
Long context reasoning (AA-LCR)Not measured67.7%Only MiniMax M2.1 measured
LMArena Text1300Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: instruction following1278Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: creative writing1291Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Vision1137Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: English1312Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: non-English1283Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Chinese1277Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: German1283Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Japanese1257Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Korean1234Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: French1304Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Spanish1292Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Russian1285Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: Polish1365Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: multi-turn1302Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: longer queries1288Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: writing and language1298Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: entertainment and media1289Not measuredOnly GPT-4o (2024-05-13) measured
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 25 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 M2.1
BenchmarkGPT-4o (2024-05-13)MiniMax M2.1Leader
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 M2.1$0.0009Fits in one request

MiniMax M2.1 costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-05-13)$0.295Fits in one request
MiniMax M2.1$0.019Fits in one request

MiniMax M2.1 costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-05-13)$1.15Exceeds the context window
MiniMax M2.1$0.072Exceeds the context window

MiniMax M2.1 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 M2.1
SpecificationOpenAI GPT-4o (2024-05-13)MiniMax M2.1
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedMay 13, 2024December 23, 2025
Context window128K205K
Input $/Mtok$5.00$0.30
Output $/Mtok$15.00$1.20
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computable$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-05-13) better than MiniMax M2.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.

Which is cheaper, GPT-4o (2024-05-13) or MiniMax M2.1?

GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.1 only.

Which has the bigger context window, GPT-4o (2024-05-13) or MiniMax M2.1?

MiniMax M2.1, with 205K tokens against 128K.

How many benchmarks is the GPT-4o (2024-05-13) vs MiniMax M2.1 comparison based on?

6 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 M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.

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