Model comparison

DeepSeek-V3.1 vs MiniMax-M2

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.4 on the Noometry Index.

Last verified October 10, 2026. 16 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

MiniMax-M2

MiniMax

37.4

Rank #204 Confirmed

Summary

They share 16 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and MiniMax-M2 in 1 category; 8 gaps are clear of the uncertainty.
The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 19.4.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.
MiniMax-M2 accepts more context: 205K tokens versus 164K.

Side by side

DeepSeek-V3.1 and MiniMax-M2 specifications
	DeepSeek-V3.1	MiniMax-M2
Provider	DeepSeek	MiniMax
Noometry Index	42.8	37.4
Released	2025-08-21	2025-10-27
Weights	Open	Open
Context window	164K	205K
Max output	8K	131K
Input $ / M tokens	$0.25	$0.30
Output $ / M tokens	$0.95	$1.20
Results tracked	27	21

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), MiniMax-M2: 39.3 (#159)

Coding benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Coding	1417	1370
SWE-bench Verified (bash only)	—	61%
LMArena WebDev	—	1297
WeirdML	38.4%	—

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, MiniMax-M2: 25.1 (#109)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
Terminal-Bench	—	30%
Vending-Bench 2	—	160.6

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), MiniMax-M2: 19.4 (#258)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
Kagi LLM Benchmark	53.2%	57.8%
LMArena Hard Prompts	1417	1357
SimpleBench	40%	—
NYT Connections (extended)	—	14.8%
DTBench	82.7%	—
LMCA	24.3%	—
Epoch Capabilities Index	139.92	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), MiniMax-M2: 37.3 (#160)

Math benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Math	1420	1352

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), MiniMax-M2: 37.0 (#163)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Expert	1405	1337
Vectara Hallucination Rate	5.5%	—

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), MiniMax-M2: 45.3 (#171)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Non-English	1400	1313
LMArena Chinese	1469	1366
LMArena French	1447	1335
LMArena German	1411	1355
LMArena Russian	1405	1331
LMArena Spanish	1431	1326
LMArena Japanese	1378	—
LMArena Korean	1337	—

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), MiniMax-M2: 70.2 (#166)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Instruction Following	1400	1328

Long Context MiniMax-M2 leads

DeepSeek-V3.1: 36.3 (#232), MiniMax-M2: 40.5 (#153)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Longer Query	1422	1331
Fiction.LiveBench	52.8%	—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), MiniMax-M2: 53.0 (#162)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	MiniMax-M2
LMArena Text	1420	1340
LMArena Creative Writing	1401	1286
LMArena Multi-Turn	1408	1361
EQ-Bench Creative Writing	1436	—

Frequently asked questions

Is DeepSeek-V3.1 better than MiniMax-M2?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.4 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or MiniMax-M2?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; MiniMax-M2 lists at $0.30 and $1.20.

Is DeepSeek-V3.1 or MiniMax-M2 better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 39.3 in the Noometry coding category.

Which has the bigger context window?

MiniMax-M2 does, with 205K tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and MiniMax-M2 share?

16 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and MiniMax-M2 has 21.