Model comparison

GPT-5 Nano vs MiniMax-M2.7

MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 33.4 on the Noometry Index. GPT-5 Nano costs 3.8× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.

Last verified October 10, 2026. 21 shared benchmarks.

GPT-5 Nano OpenAI

33.4

Rank #234 Confirmed

MiniMax-M2.7

MiniMax

37.4

Rank #188 Confirmed

Summary

They share 21 benchmarks with published results for both. GPT-5 Nano scores higher in 3 categories and MiniMax-M2.7 in 6 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where MiniMax-M2.7 leads 59.1 to 39.4.
The biggest single-benchmark swing is Terminal-Bench: 21.8% for GPT-5 Nano and 45.1% for MiniMax-M2.7.
GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
GPT-5 Nano accepts more context: 400K tokens versus 205K.
MiniMax-M2.7 has downloadable open weights; the other is API-only.

Side by side

GPT-5 Nano and MiniMax-M2.7 specifications
	GPT-5 Nano	MiniMax-M2.7
Provider	OpenAI	MiniMax
Noometry Index	33.4	37.4
Released	2025-08-07	2026-03-18
Weights	Proprietary	Open
Context window	400K	205K
Max output	128K	131K
Input $ / M tokens	$0.05	$0.30
Output $ / M tokens	$0.40	$1.20
Results tracked	48	30

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Category by category

Coding MiniMax-M2.7 leads

GPT-5 Nano: 33.3 (#248), MiniMax-M2.7: 41.4 (#115)

Coding benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
WeirdML	38.1%	37%
LMArena Coding	1351	1454
ALE-Bench	718.67	599.25
SWE-bench Verified (bash only)	34.8%	—
LMArena WebDev	—	1397
SciCode	—	47%

Agentic & Tool Use Too close to call

GPT-5 Nano: 25.6 (#99), MiniMax-M2.7: 24.9 (#103)

Agentic & Tool Use benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
Terminal-Bench	21.8%	45.1%
Berkeley Function Calling Leaderboard	51.5%	—
ExploitBench	—	13.3%
GBAEval	—	0%

Reasoning MiniMax-M2.7 leads

GPT-5 Nano: 16.4 (#291), MiniMax-M2.7: 19.0 (#248)

Reasoning benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Hard Prompts	1329	1422
Epoch Capabilities Index	139.39	145.84
ARC-AGI-2	2.6%	—
Kagi LLM Benchmark	62.2%	—
NYT Connections (extended)	—	24.7%
ARC-AGI-1	20.7%	—
CritPt	—	0.6%
Chess Puzzles	27%	—
Thematic Generalization	—	39.3%
Mystery Game Puzzles	9%	—
DTBench	62.7%	—
LMCA	7.9%	—
ForecastBench	59.1	—

Math GPT-5 Nano leads

GPT-5 Nano: 29.1 (#221), MiniMax-M2.7: 24.9 (#254)

Math benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
ProofBench	12%	3%
LMArena Math	1318	1420
FrontierMath (Tiers 1-3)	20%	—
FrontierMath Tier 4	2.4%	—
OTIS Mock AIME 2024-2025	81.1%	—
Omni-MATH	54.6%	—
MATH Level 5	95.2%	—
FrontierMath (Feb 2025 set)	8.3%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge MiniMax-M2.7 leads

GPT-5 Nano: 35.8 (#166), MiniMax-M2.7: 37.1 (#151)

Knowledge benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
Vectara Hallucination Rate	10.5%	12.9%
LMArena Expert	1321	1443
GPQA Diamond	69.4%	—
SimpleQA Verified	11.7%	—
MMLU-Pro	77.8%	—
GPQA (HELM)	67.9%	—

Multimodal Not comparable

GPT-5 Nano: 30.7 (#102), MiniMax-M2.7: —

Multimodal benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Vision	1159	—
VPCT	37.2%	—

Multilingual MiniMax-M2.7 leads

GPT-5 Nano: 45.5 (#167), MiniMax-M2.7: 50.5 (#120)

Multilingual benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Non-English	1313	1382
LMArena Chinese	1355	1439
LMArena German	1329	1398
LMArena Korean	1269	1313
LMArena Russian	1296	1383
LMArena Spanish	1360	1403
LMArena French	—	1422
LMArena Japanese	—	1261

Instruction Following Too close to call

GPT-5 Nano: 75.2 (#72), MiniMax-M2.7: 74.2 (#101)

Instruction Following benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Instruction Following	1306	1404
IFEval	93.2%	—

Long Context MiniMax-M2.7 leads

GPT-5 Nano: 31.3 (#275), MiniMax-M2.7: 43.3 (#94)

Long Context benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Longer Query	1313	1419
Fiction.LiveBench	44.4%	—

Writing & Preference MiniMax-M2.7 leads

GPT-5 Nano: 39.4 (#244), MiniMax-M2.7: 59.1 (#107)

Writing & Preference benchmarks
Benchmark	GPT-5 Nano	MiniMax-M2.7
LMArena Text	1320	1404
LMArena Creative Writing	1250	1354
LMArena Multi-Turn	1311	1413
EQ-Bench Creative Writing	705	—
WildBench	80.6%	—

Frequently asked questions

Is GPT-5 Nano better than MiniMax-M2.7?

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

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.

Is GPT-5 Nano or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 33.3 in the Noometry coding category.

Which has the bigger context window?

GPT-5 Nano does, with 400K tokens against 205K.

How many benchmarks do GPT-5 Nano and MiniMax-M2.7 share?

21 benchmarks have published results for both models. GPT-5 Nano has 48 scored results on Noometry and MiniMax-M2.7 has 30.