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DeepSeek V3.2 vs MiniMax M2.1

DeepSeek V3.2 leads MiniMax M2.1 on the Artificial Analysis Intelligence Index, 21.5 to 20.9, and DeepSeek V3.2 costs 1.4x less per output token ($0.88 vs $1.20 per million). Updated October 22, 2026; 30 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace MiniMax M2.1
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
15
MiniMax M2.1MiniMax
Coding
No data
Overall
No data
Output $/M
$1.20
15
vs boards won of 30 shared

DeepSeek V3.2 or MiniMax M2.1: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding.

Pick MiniMax M2.1 for a lower cost per solved task ($0.59 against $0.99) and a larger context window (205K against 164K tokens).

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BMiniMax M2.1MiniMaxNot benchmarked1 independent benchmark$0.59 per solved task
Shared results
30
DeepSeek V3.2 only
45
MiniMax M2.1 only
4
Like-for-like categories
8 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
DeepSeek V3.2 30.0 from 3 benchmarks, MiniMax M2.1 not benchmarked.
Widest category
General, 7.4 points apart on average over 2 shared benchmarks.
Widest single gap
Design Arena: SVG: DeepSeek V3.2 1032 against MiniMax M2.1 1131, 12.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against MiniMax M2.1 $1.20 per million output tokens, 1.4x apart.
Context window
DeepSeek V3.2 takes 164K 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
CategoryDeepSeek V3.2BarsMiniMax M2.1Ties
Coding32
Intelligence10
Agentic work10
Reasoning30
Math31
Knowledge34
Web design07
General11
All shared boards1515
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
Coding benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev1362Not measuredOnly DeepSeek V3.2 measured
AA Coding Index44.2Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Vals AI)80.7%81.8%MiniMax M2.1
SWE-bench Verified (Vals AI)67.6%74.8%MiniMax M2.1
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%81.0%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)35.6%28.8%DeepSeek V3.2
Terminal-Bench39.6%36.6%DeepSeek V3.2
ALE-BenchNot measured623.8Only MiniMax M2.1 measured
LMArena Text: coding1453Not measuredOnly DeepSeek V3.2 measured
LMArena Text: software and IT1449Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML1371Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
AA Intelligence Index21.520.9DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
τ²-Bench Telecom90.6%85.4%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
GPQA Diamond80.3%78.5%DeepSeek V3.2
GPQA Diamond (Artificial Analysis run)84.0%83.0%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%23.2%DeepSeek V3.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts1434Not measuredOnly DeepSeek V3.2 measured
LMArena Text: expert1436Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts in English1446Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index31
Math benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
AIME84.6%77.9%DeepSeek V3.2
MATH 500 (Vals AI)Not measured89.0%Only MiniMax M2.1 measured
MGSM (Vals AI)86.0%87.8%MiniMax M2.1
AA Math Index92.082.7DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%82.7%DeepSeek V3.2
LMArena Text: math1435Not measuredOnly DeepSeek V3.2 measured
LMArena Text: mathematical work1440Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index34
Knowledge benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
MMLU Pro84.9%87.0%MiniMax M2.1
LegalBench (Vals AI)76.1%75.5%DeepSeek V3.2
CaseLaw (Vals AI)55.4%55.8%MiniMax M2.1
CorpFin (Vals AI)51.0%58.9%MiniMax M2.1
TaxEval (Vals AI)68.2%66.3%DeepSeek V3.2
MedQA (Vals AI)93.9%91.2%DeepSeek V3.2
MedCode (Vals AI)Not measured34.1%Only MiniMax M2.1 measured
MMLU Pro (Artificial Analysis run)86.2%87.5%MiniMax M2.1
LMArena Text: science1446Not measuredOnly DeepSeek V3.2 measured
LMArena Text: medicine and healthcare1440Not measuredOnly DeepSeek V3.2 measured
LMArena Text: legal and government1425Not measuredOnly DeepSeek V3.2 measured
LMArena Text: business and finance1410Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
Design Arena: websites11751201MiniMax M2.1
Design Arena: UI components11461220MiniMax M2.1
Design Arena: game dev11311134MiniMax M2.1
Design Arena: data viz11581206MiniMax M2.1
Design Arena: 3D11321164MiniMax M2.1
Design Arena: all code categories11631190MiniMax M2.1
Design Arena: SVG10321131MiniMax M2.1
Design Arena: ASCII art1083Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
General benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax M2.1Leader
MedScribe (Vals AI)Not measured80.8%Only MiniMax M2.1 measured
IFBench60.7%69.9%MiniMax M2.1
Long context reasoning (AA-LCR)73.3%67.7%DeepSeek V3.2
DTBench85.6%Not measuredOnly DeepSeek V3.2 measured
LMCA28.8%Not measuredOnly DeepSeek V3.2 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text1425Not measuredOnly DeepSeek V3.2 measured
LMArena Text: instruction following1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: creative writing1400Not measuredOnly DeepSeek V3.2 measured
LMArena Text: English1439Not measuredOnly DeepSeek V3.2 measured
LMArena Text: non-English1409Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Chinese1455Not measuredOnly DeepSeek V3.2 measured
LMArena Text: German1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Japanese1374Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Korean1370Not measuredOnly DeepSeek V3.2 measured
LMArena Text: French1429Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Spanish1424Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Russian1424Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Polish1419Not measuredOnly DeepSeek V3.2 measured
LMArena Text: multi-turn1427Not measuredOnly DeepSeek V3.2 measured
LMArena Text: longer queries1428Not measuredOnly DeepSeek V3.2 measured
LMArena Text: writing and language1407Not measuredOnly DeepSeek V3.2 measured
LMArena Text: entertainment and media1395Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and MiniMax M2.1
BenchmarkDeepSeek V3.2MiniMax 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

DeepSeek V3.2$0.0007Fits in one request
MiniMax M2.1$0.0009Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
MiniMax M2.1$0.019Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
MiniMax M2.1$0.072Exceeds the context window

DeepSeek V3.2 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 DeepSeek V3.2 and MiniMax M2.1
SpecificationDeepSeek V3.2MiniMax M2.1
MakerDeepSeekMiniMax
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025December 23, 2025
Context window164K205K
Input $/Mtok$0.29$0.30
Output $/Mtok$0.88$1.20
Coding index30.0Not benchmarked
Independent measurements behind it31
Cost per solved task$0.99$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than MiniMax M2.1 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding. Pick MiniMax M2.1 for a lower cost per solved task ($0.59 against $0.99) and a larger context window (205K against 164K tokens).

Which is cheaper, DeepSeek V3.2 or MiniMax M2.1?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and MiniMax M2.1 $0.59.

Which has the bigger context window, DeepSeek V3.2 or MiniMax M2.1?

MiniMax M2.1, with 205K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs MiniMax M2.1 comparison based on?

30 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2'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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