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GPT-5.2-Codex vs MiniMax M2.1

GPT-5.2-Codex leads MiniMax M2.1 on the Artificial Analysis Intelligence Index, 28.5 to 20.9, and MiniMax M2.1 costs 11.7x less per output token ($1.20 vs $14.00 per million). Updated October 22, 2026; 11 benchmarks have a result for both.

Replace GPT-5.2-Codex Replace MiniMax M2.1
GPT-5.2-CodexOpenAI
Coding
45.4
Overall
No data
Output $/M
$14.00
10
MiniMax M2.1MiniMax
Coding
No data
Overall
No data
Output $/M
$1.20
1
vs boards won of 11 shared

GPT-5.2-Codex or MiniMax M2.1: which to pick

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding.

Pick MiniMax M2.1 for output tokens 11.7x cheaper ($1.20 vs $14.00 per million) and work measured by SWE-bench Verified (Vals AI), where it leads.

Model AGPT-5.2-CodexOpenAI45.4/1003 independent benchmarks$0.43 per solved taskModel BMiniMax M2.1MiniMaxNot benchmarked1 independent benchmark$0.59 per solved task
Shared results
11
GPT-5.2-Codex only
11
MiniMax M2.1 only
23
Like-for-like categories
5 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-5.2-Codex 45.4 from 3 benchmarks, MiniMax M2.1 not benchmarked.
Widest category
Coding, 13.9 points apart on average over 5 shared benchmarks.
Widest single gap
Terminal-Bench: GPT-5.2-Codex 66.5% against MiniMax M2.1 36.6%, 29.9 points apart on a 0 to 100 scale.
Output price
GPT-5.2-Codex $14.00 against MiniMax M2.1 $1.20 per million output tokens, 11.7x apart.
Context window
GPT-5.2-Codex takes 400K 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-5.2-CodexBarsMiniMax M2.1Ties
Coding41
Intelligence10
Agentic work10
Reasoning20
General20
All shared boards101
CodingLike-for-likeSix of these boards feed the Vibe Coding Index41
Coding benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax M2.1Leader
SWE-bench Verified72.8%Not measuredOnly GPT-5.2-Codex measured
SWE-bench Multilingual66.3%Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev1339Not measuredOnly GPT-5.2-Codex measured
LiveCodeBench (Vals AI)88.0%81.8%GPT-5.2-Codex
SWE-bench Verified (Vals AI)72.4%74.8%MiniMax M2.1
Vibe Code Bench (Vals AI)37.9%Not measuredOnly GPT-5.2-Codex measured
LiveCodeBench (Artificial Analysis run)Not measured81.0%Only MiniMax M2.1 measured
Terminal-Bench Hard (Artificial Analysis run)37.1%28.8%GPT-5.2-Codex
Terminal-Bench66.5%36.6%GPT-5.2-Codex
ALE-Bench1299.9623.8GPT-5.2-Codex
LMArena WebDev: React1329Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev: HTML1348Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax M2.1Leader
AA Intelligence Index28.520.9GPT-5.2-Codex
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax M2.1Leader
Design Arena: agents building web apps1051Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building full-stack apps993Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building mobile apps1090Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Android apps1176Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Godot games1142Not measuredOnly GPT-5.2-Codex measured
τ²-Bench Telecom92.1%85.4%GPT-5.2-Codex
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax M2.1Leader
GPQA DiamondNot measured78.5%Only MiniMax M2.1 measured
GPQA Diamond (Artificial Analysis run)89.9%83.0%GPT-5.2-Codex
Humanity's Last Exam (Artificial Analysis run)35.7%23.2%GPT-5.2-Codex
Neither model has a result on the other 19 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Math benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax 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
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Knowledge benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax 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)Not measured87.5%Only MiniMax M2.1 measured
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax 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
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax M2.1Leader
MedScribe (Vals AI)Not measured80.8%Only MiniMax M2.1 measured
IFBench77.6%69.9%GPT-5.2-Codex
Long context reasoning (AA-LCR)82.3%67.7%GPT-5.2-Codex
Neither model has a result on the other 43 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.2-Codex and MiniMax M2.1
BenchmarkGPT-5.2-CodexMiniMax 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-5.2-Codex$0.0088Fits in one request
MiniMax M2.1$0.0009Fits in one request

MiniMax M2.1 costs less

Repository review

50K input + 3K output tokens

GPT-5.2-Codex$0.130Fits in one request
MiniMax M2.1$0.019Fits in one request

MiniMax M2.1 costs less

Agent loop

200K input + 10K output tokens

GPT-5.2-Codex$0.490Fits in one request
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-5.2-Codex and MiniMax M2.1
SpecificationOpenAI GPT-5.2-CodexMiniMax M2.1
MakerOpenAIMiniMax
WeightsNo public weights listedOpen weights
ReleasedJanuary 14, 2026December 23, 2025
Context window400K205K
Input $/Mtok$1.75$0.30
Output $/Mtok$14.00$1.20
Coding index45.4Not benchmarked
Independent measurements behind it31
Cost per solved task$0.43$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.2-Codex better than MiniMax M2.1 for coding?

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding. Pick MiniMax M2.1 for output tokens 11.7x cheaper ($1.20 vs $14.00 per million) and work measured by SWE-bench Verified (Vals AI), where it leads.

Which is cheaper, GPT-5.2-Codex or MiniMax M2.1?

GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GPT-5.2-Codex costs $0.43 and MiniMax M2.1 $0.59.

Which has the bigger context window, GPT-5.2-Codex or MiniMax M2.1?

GPT-5.2-Codex, with 400K tokens against 205K.

How many benchmarks is the GPT-5.2-Codex vs MiniMax M2.1 comparison based on?

11 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 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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