Model comparison

GPT-5.2 Codex vs MiniMax-M2.1

GPT-5.2 Codex is the stronger model overall, scoring 42.2 to 38.4 on the Noometry Index. MiniMax-M2.1 costs 9.2× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

Last verified . 3 shared benchmarks.

GPT-5.2 Codex OpenAI

42.2

Rank #107 Reported

MiniMax-M2.1 MiniMax

38.4

Rank #177 Confirmed

Summary

  • They share 3 benchmarks with published results for both. GPT-5.2 Codex scores higher in 2 categories and MiniMax-M2.1 in 0 categories; 2 gaps are clear of the uncertainty.
  • The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 40.7 to 27.7.
  • The biggest single-benchmark swing is Terminal-Bench: 66.5% for GPT-5.2 Codex and 36.6% for MiniMax-M2.1.
  • MiniMax-M2.1 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
  • GPT-5.2 Codex accepts more context: 400K tokens versus 205K.
  • MiniMax-M2.1 has downloadable open weights; the other is API-only.

Side by side

GPT-5.2 Codex and MiniMax-M2.1 specifications
GPT-5.2 CodexMiniMax-M2.1
ProviderOpenAIMiniMax
Noometry Index42.238.4
Released2025-12-182025-12-23
WeightsProprietaryOpen
Context window400K205K
Max output128K131K
Input $ / M tokens$1.75$0.30
Output $ / M tokens$14$1.20
Results tracked522

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Category by category

Coding GPT-5.2 Codex leads

GPT-5.2 Codex: 45.4 (#66), MiniMax-M2.1: 40.1 (#132)

Coding benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena WebDev13391384
ALE-Bench1,300623.83
SWE-bench Verified (bash only)72.8%—
SWE-bench Multilingual66.3%—
LMArena Coding—1421

Agentic & Tool Use GPT-5.2 Codex leads

GPT-5.2 Codex: 40.7 (#20), MiniMax-M2.1: 27.7 (#91)

Agentic & Tool Use benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
Terminal-Bench66.5%36.6%

Reasoning Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 15.8 (#301)

Reasoning benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
NYT Connections (extended)—11.2%
LMArena Hard Prompts—1410

Math Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 35.6 (#142)

Math benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena Math—1396

Knowledge Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 37.7 (#143)

Knowledge benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
Vectara Hallucination Rate—11.8%
LMArena Expert—1432

Multilingual Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 50.2 (#123)

Multilingual benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena Non-English—1378
LMArena Chinese—1430
LMArena French—1405
LMArena German—1380
LMArena Japanese—1287
LMArena Korean—1297
LMArena Russian—1386
LMArena Spanish—1396

Instruction Following Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 74.0 (#108)

Instruction Following benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena Instruction Following—1399

Long Context Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 43.2 (#98)

Long Context benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena Longer Query—1416

Writing & Preference Not comparable

GPT-5.2 Codex: —, MiniMax-M2.1: 58.5 (#114)

Writing & Preference benchmarks
BenchmarkGPT-5.2 CodexMiniMax-M2.1
LMArena Text—1392
LMArena Creative Writing—1362
LMArena Multi-Turn—1396

Frequently asked questions

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

GPT-5.2 Codex is the stronger model overall, scoring 42.2 to 38.4 on the Noometry Index. MiniMax-M2.1 costs 9.2× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

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

MiniMax-M2.1 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

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

GPT-5.2 Codex scores higher on coding benchmarks: 45.4 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do GPT-5.2 Codex and MiniMax-M2.1 share?

3 benchmarks have published results for both models. GPT-5.2 Codex has 5 scored results on Noometry and MiniMax-M2.1 has 22.

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