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MiniMax M2.7 vs Qwen3.5 397B A17B

Updated October 21, 2026. 48 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace MiniMax M2.7 Replace Qwen3.5 397B A17B
MiniMax M2.7MiniMax
Coding
28.4
Overall
22.8
Output $/M
$1.20
11
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
37
vs boards won of 48 shared

Decision reading

MiniMax M2.7 and Qwen3.5 397B A17B are level for coding, 28.4 against 28.6 on the Vibe Coding Index, so price and context decide.

Pick MiniMax M2.7 when output tokens cost 1.9x less, or when what matters is SciCode or LMArena WebDev: React, where it leads. Pick Qwen3.5 397B A17B when the context window is larger, 262K against 205K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AMiniMax M2.7MiniMax28.4/1001 independent benchmark$0.64 per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
48
MiniMax M2.7 only
26
Qwen3.5 397B A17B only
17
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
MiniMax M2.7 28.4 from 1 benchmark, Qwen3.5 397B A17B 28.6 from 1 benchmark.
Widest category
Agentic work, 10.8 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Japanese: MiniMax M2.7 1260 against Qwen3.5 397B A17B 1429, 28.2 points apart on a 0 to 100 scale.
Output price
MiniMax M2.7 $1.20 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.9x apart.
Context window
MiniMax M2.7 takes 205K tokens, Qwen3.5 397B A17B 262K.

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
CategoryMiniMax M2.7BarsQwen3.5 397B A17BTies
Coding26
Intelligence11
Agentic work01
Reasoning14
Math02
Knowledge04
Web design61
General118
All shared boards1137
CodingLike-for-likeFour of these boards feed the Vibe Coding Index26
Coding benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
LMArena WebDev13991400Qwen3.5 397B A17B
AA Coding Index52.6Not measuredOnly MiniMax M2.7 measured
LiveCodeBench (Vals AI)79.9%Not measuredOnly MiniMax M2.7 measured
SWE-bench Verified (Vals AI)73.8%Not measuredOnly MiniMax M2.7 measured
Vibe Code Bench (Vals AI)11.9%Not measuredOnly MiniMax M2.7 measured
Code migration (Vals AI)8.7%Not measuredOnly MiniMax M2.7 measured
ProgramBench (Vals AI)0.0%Not measuredOnly MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)48.7%Not measuredOnly MiniMax M2.7 measured
SciCode50.1%44.8%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)39.4%40.9%Qwen3.5 397B A17B
Terminal-Bench45.1%Not measuredOnly MiniMax M2.7 measured
WeirdML37.0%Not measuredOnly MiniMax M2.7 measured
ALE-Bench599.3621.9Qwen3.5 397B A17B
LMArena Text: coding14541464Qwen3.5 397B A17B
LMArena Text: software and IT14461463Qwen3.5 397B A17B
LMArena WebDev: React13881388MiniMax M2.7
LMArena WebDev: HTML14011410Qwen3.5 397B A17B
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
AA Intelligence Index22.821.4MiniMax M2.7
Epoch Capabilities Index145.8146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
AA Agentic Index15.3Not measuredOnly MiniMax M2.7 measured
Finance Agent (Vals AI)27.9%Not measuredOnly MiniMax M2.7 measured
Tax Agent Bench (Vals AI)6.9%Not measuredOnly MiniMax M2.7 measured
Legal Research Bench (Vals AI)10.6%Not measuredOnly MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)28.4%Not measuredOnly MiniMax M2.7 measured
τ²-Bench Telecom84.8%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
GPQA Diamond86.6%Not measuredOnly MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)87.4%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)29.6%29.0%MiniMax M2.7
GPQA Diamond (Epoch AI run)Not measured86.4%Only Qwen3.5 397B A17B measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14221448Qwen3.5 397B A17B
LMArena Text: expert14431462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14411450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
AIME91.0%Not measuredOnly MiniMax M2.7 measured
OTIS Mock AIMENot measured88.9%Only Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
ProofBench3.0%Not measuredOnly MiniMax M2.7 measured
LMArena Text: math14211453Qwen3.5 397B A17B
LMArena Text: mathematical work14371454Qwen3.5 397B A17B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
MMLU Pro80.4%Not measuredOnly MiniMax M2.7 measured
LegalBench (Vals AI)84.0%Not measuredOnly MiniMax M2.7 measured
CaseLaw (Vals AI)60.9%Not measuredOnly MiniMax M2.7 measured
CorpFin (Vals AI)61.2%Not measuredOnly MiniMax M2.7 measured
TaxEval (Vals AI)66.6%Not measuredOnly MiniMax M2.7 measured
MedCode (Vals AI)34.4%Not measuredOnly MiniMax M2.7 measured
LMArena Text: science14161457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14091455Qwen3.5 397B A17B
LMArena Text: legal and government14031441Qwen3.5 397B A17B
LMArena Text: business and finance14071435Qwen3.5 397B A17B
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index61
Web design benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
Design Arena: websites12431191MiniMax M2.7
Design Arena: UI components12101163MiniMax M2.7
Design Arena: game dev12101141MiniMax M2.7
Design Arena: data viz12331175MiniMax M2.7
Design Arena: 3D11951161MiniMax M2.7
Design Arena: all code categories12331180MiniMax M2.7
Design Arena: SVG11361139Qwen3.5 397B A17B
Design Arena: ASCII art1136Not measuredOnly MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
General benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
MedScribe (Vals AI)79.9%Not measuredOnly MiniMax M2.7 measured
IFBench75.7%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)78.3%77.3%MiniMax M2.7
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
ExploitBench13.3%Not measuredOnly MiniMax M2.7 measured
LMArena Text14051437Qwen3.5 397B A17B
LMArena Text: instruction following14051424Qwen3.5 397B A17B
LMArena Text: creative writing13541402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14261443Qwen3.5 397B A17B
LMArena Text: non-English13821430Qwen3.5 397B A17B
LMArena Text: Chinese14421498Qwen3.5 397B A17B
LMArena Text: German13981445Qwen3.5 397B A17B
LMArena Text: Japanese12601429Qwen3.5 397B A17B
LMArena Text: Korean13141384Qwen3.5 397B A17B
LMArena Text: French14241462Qwen3.5 397B A17B
LMArena Text: Spanish14021441Qwen3.5 397B A17B
LMArena Text: Russian13831430Qwen3.5 397B A17B
LMArena Text: Polish13871447Qwen3.5 397B A17B
LMArena Text: multi-turn14121446Qwen3.5 397B A17B
LMArena Text: longer queries14181441Qwen3.5 397B A17B
LMArena Text: writing and language13761415Qwen3.5 397B A17B
LMArena Text: entertainment and media13601399Qwen3.5 397B A17B
LMArena Vision: EnglishNot measured1268Only Qwen3.5 397B A17B measured
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for MiniMax M2.7 and Qwen3.5 397B A17B
BenchmarkMiniMax M2.7Qwen3.5 397B A17BLeader
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

MiniMax M2.7$0.0009Fits in one request
Qwen3.5 397B A17B$0.0016Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

MiniMax M2.7$0.019Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

MiniMax M2.7$0.072Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

MiniMax M2.7 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 MiniMax M2.7 and Qwen3.5 397B A17B
SpecificationMiniMax M2.7Qwen3.5 397B A17B
MakerMiniMaxQwen
WeightsOpen weightsOpen weights
ReleasedMarch 18, 2026February 16, 2026
Context window205K262K
Input $/Mtok$0.30$0.39
Output $/Mtok$1.20$2.34
Coding index28.428.6
Independent measurements behind it11
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is MiniMax M2.7 better than Qwen3.5 397B A17B for coding?

MiniMax M2.7 and Qwen3.5 397B A17B are level for coding, 28.4 against 28.6 on the Vibe Coding Index, so price and context decide. Pick MiniMax M2.7 when output tokens cost 1.9x less, or when what matters is SciCode or LMArena WebDev: React, where it leads. Pick Qwen3.5 397B A17B when the context window is larger, 262K against 205K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, MiniMax M2.7 or Qwen3.5 397B A17B?

MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, MiniMax M2.7 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 205K.

How many benchmarks is the MiniMax M2.7 vs Qwen3.5 397B A17B comparison based on?

48 benchmarks have a result for both models as of October 21, 2026. MiniMax M2.7's coding index rests on 1 independent benchmark and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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