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GPT-5.1-Codex-Mini vs MiniMax M2.7

MiniMax M2.7 leads GPT-5.1-Codex-Mini on the Artificial Analysis Intelligence Index, 22.8 to 20.4, and MiniMax M2.7 costs 1.7x less per output token ($1.20 vs $2.00 per million). Updated October 22, 2026; 18 benchmarks have a result for both.

Replace GPT-5.1-Codex-Mini Replace MiniMax M2.7
GPT-5.1-Codex-MiniOpenAI
Coding
No data
Overall
No data
Output $/M
$2.00
1
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
17
vs boards won of 18 shared

GPT-5.1-Codex-Mini or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-5.1-Codex-Mini has none, so the two cannot be ranked on coding.

Pick GPT-5.1-Codex-Mini for a larger context window (400K against 205K tokens) and work measured by Terminal-Bench, where it leads.

Model AGPT-5.1-Codex-MiniOpenAINot benchmarked1 independent benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
18
GPT-5.1-Codex-Mini only
4
MiniMax M2.7 only
56
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-5.1-Codex-Mini not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 21.9 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: 3D: GPT-5.1-Codex-Mini 988 against MiniMax M2.7 1195, 25.9 points apart on a 0 to 100 scale.
Output price
GPT-5.1-Codex-Mini $2.00 against MiniMax M2.7 $1.20 per million output tokens, 1.7x apart.
Context window
GPT-5.1-Codex-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-5.1-Codex-MiniBarsMiniMax M2.7Ties
Coding13
Intelligence01
Agentic work01
Reasoning02
Web design08
General02
All shared boards117
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
Coding benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
LMArena WebDev12441399MiniMax M2.7
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
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)83.6%Not measuredOnly GPT-5.1-Codex-Mini measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)33.3%39.4%MiniMax M2.7
Terminal-Bench61.6%45.1%GPT-5.1-Codex-Mini
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: codingNot measured1454Only MiniMax M2.7 measured
LMArena Text: software and ITNot measured1446Only MiniMax M2.7 measured
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTML12531401MiniMax M2.7
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
AA Intelligence Index20.422.8MiniMax M2.7
Epoch Capabilities IndexNot measured145.8Only MiniMax M2.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-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 Telecom62.9%84.8%MiniMax M2.7
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)81.3%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)18.5%29.6%MiniMax M2.7
LMArena Text: hard promptsNot measured1422Only MiniMax M2.7 measured
LMArena Text: expertNot measured1443Only MiniMax M2.7 measured
LMArena Text: hard prompts in EnglishNot measured1441Only MiniMax M2.7 measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
AA Math Index91.7Not measuredOnly GPT-5.1-Codex-Mini measured
AIME 2025 (Artificial Analysis run)91.7%Not measuredOnly GPT-5.1-Codex-Mini measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
LMArena Text: mathNot measured1421Only MiniMax M2.7 measured
LMArena Text: mathematical workNot measured1437Only MiniMax M2.7 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Knowledge benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax 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)82.0%Not measuredOnly GPT-5.1-Codex-Mini measured
LMArena Text: scienceNot measured1416Only MiniMax M2.7 measured
LMArena Text: medicine and healthcareNot measured1409Only MiniMax M2.7 measured
LMArena Text: legal and governmentNot measured1403Only MiniMax M2.7 measured
LMArena Text: business and financeNot measured1407Only MiniMax M2.7 measured
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
Design Arena: websites11111243MiniMax M2.7
Design Arena: UI components10781210MiniMax M2.7
Design Arena: game dev10921209MiniMax M2.7
Design Arena: data viz10971233MiniMax M2.7
Design Arena: 3D9881195MiniMax M2.7
Design Arena: all code categories10931233MiniMax M2.7
Design Arena: SVG9781137MiniMax M2.7
Design Arena: ASCII art11051136MiniMax M2.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GPT-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-MiniMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench67.9%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)66.7%78.3%MiniMax M2.7
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena TextNot measured1405Only MiniMax M2.7 measured
LMArena Text: instruction followingNot measured1405Only MiniMax M2.7 measured
LMArena Text: creative writingNot measured1354Only MiniMax M2.7 measured
LMArena Text: EnglishNot measured1426Only MiniMax M2.7 measured
LMArena Text: non-EnglishNot measured1382Only MiniMax M2.7 measured
LMArena Text: ChineseNot measured1442Only MiniMax M2.7 measured
LMArena Text: GermanNot measured1398Only MiniMax M2.7 measured
LMArena Text: JapaneseNot measured1260Only MiniMax M2.7 measured
LMArena Text: KoreanNot measured1314Only MiniMax M2.7 measured
LMArena Text: FrenchNot measured1424Only MiniMax M2.7 measured
LMArena Text: SpanishNot measured1402Only MiniMax M2.7 measured
LMArena Text: RussianNot measured1383Only MiniMax M2.7 measured
LMArena Text: PolishNot measured1387Only MiniMax M2.7 measured
LMArena Text: multi-turnNot measured1412Only MiniMax M2.7 measured
LMArena Text: longer queriesNot measured1418Only MiniMax M2.7 measured
LMArena Text: writing and languageNot measured1376Only MiniMax M2.7 measured
LMArena Text: entertainment and mediaNot measured1360Only MiniMax M2.7 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-5.1-Codex-Mini and MiniMax M2.7
BenchmarkGPT-5.1-Codex-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-5.1-Codex-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-5.1-Codex-Mini$0.018Fits in one request
MiniMax M2.7$0.019Fits in one request

GPT-5.1-Codex-Mini costs less

Agent loop

200K input + 10K output tokens

GPT-5.1-Codex-Mini$0.070Fits in one request
MiniMax M2.7$0.072Exceeds the context window

GPT-5.1-Codex-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-5.1-Codex-Mini and MiniMax M2.7
SpecificationOpenAI GPT-5.1-Codex-MiniMiniMax M2.7
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedNovember 13, 2025March 18, 2026
Context window400K205K
Input $/Mtok$0.25$0.30
Output $/Mtok$2.00$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it13
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.1-Codex-Mini better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-5.1-Codex-Mini has none, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for a larger context window (400K against 205K tokens) and work measured by Terminal-Bench, where it leads.

Which is cheaper, GPT-5.1-Codex-Mini or MiniMax M2.7?

GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 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-5.1-Codex-Mini or MiniMax M2.7?

GPT-5.1-Codex-Mini, with 400K tokens against 205K.

How many benchmarks is the GPT-5.1-Codex-Mini vs MiniMax M2.7 comparison based on?

18 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark 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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