Enter opens the highlighted result. Esc closes.

MiniMax M2.7 vs Qwen3.5-122B-A10B

MiniMax M2.7 leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 22.8 to 17.7, and MiniMax M2.7 costs 1.7x less per output token ($1.20 vs $2.08 per million). Updated October 22, 2026; 40 benchmarks have a result for both.

Replace MiniMax M2.7 Replace Qwen3.5-122B-A10B
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
18
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
21
vs boards won of 40 shared, 1 tied

MiniMax M2.7 or Qwen3.5-122B-A10B: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-122B-A10B for a larger context window (262K against 205K tokens) and work measured by τ²-Bench Telecom or LMArena Text: math, where it leads.

Model AMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
40
MiniMax M2.7 only
34
Qwen3.5-122B-A10B only
11
Like-for-like categories
7 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
MiniMax M2.7 36.2 from 3 benchmarks, Qwen3.5-122B-A10B not benchmarked.
Widest category
Agentic work, 8.8 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Japanese: MiniMax M2.7 1260 against Qwen3.5-122B-A10B 1367, 17.8 points apart on a 0 to 100 scale.
Output price
MiniMax M2.7 $1.20 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.7x apart.
Context window
MiniMax M2.7 takes 205K tokens, Qwen3.5-122B-A10B 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 M2.7BarsQwen3.5-122B-A10BTies
Coding80
Intelligence10
Agentic work01
Reasoning50
Math11
Knowledge04
General3151 tie
All shared boards18211 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
Coding benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
LMArena WebDev13991361MiniMax M2.7
AA Coding Index52.643.3MiniMax M2.7
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%39.7%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)39.4%31.1%MiniMax M2.7
Terminal-Bench45.1%Not measuredOnly MiniMax M2.7 measured
WeirdML37.0%Not measuredOnly MiniMax M2.7 measured
ALE-Bench599.3Not measuredOnly MiniMax M2.7 measured
LMArena Text: coding14541436MiniMax M2.7
LMArena Text: software and IT14461436MiniMax M2.7
LMArena WebDev: React13881353MiniMax M2.7
LMArena WebDev: HTML14011351MiniMax M2.7
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
AA Intelligence Index22.817.7MiniMax M2.7
Epoch Capabilities Index145.8Not measuredOnly MiniMax M2.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
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%93.6%Qwen3.5-122B-A10B
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
GPQA Diamond86.6%Not measuredOnly MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)87.4%85.7%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)29.6%25.2%MiniMax M2.7
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts14221421MiniMax M2.7
LMArena Text: expert14431431MiniMax M2.7
LMArena Text: hard prompts in English14411431MiniMax M2.7
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index11
Math benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
AIME91.0%Not measuredOnly MiniMax M2.7 measured
ProofBench3.0%Not measuredOnly MiniMax M2.7 measured
LMArena Text: math14211431Qwen3.5-122B-A10B
LMArena Text: mathematical work14371431MiniMax M2.7
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
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: science14161432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare14091423Qwen3.5-122B-A10B
LMArena Text: legal and government14031422Qwen3.5-122B-A10B
LMArena Text: business and finance14071414Qwen3.5-122B-A10B
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
Design Arena: websites1242Not measuredOnly MiniMax M2.7 measured
Design Arena: UI components1207Not measuredOnly MiniMax M2.7 measured
Design Arena: game dev1209Not measuredOnly MiniMax M2.7 measured
Design Arena: data viz1233Not measuredOnly MiniMax M2.7 measured
Design Arena: 3D1194Not measuredOnly MiniMax M2.7 measured
Design Arena: all code categories1233Not measuredOnly MiniMax M2.7 measured
Design Arena: SVG1137Not measuredOnly MiniMax M2.7 measured
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 index315
General benchmarks for MiniMax M2.7 and Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
MedScribe (Vals AI)79.9%Not measuredOnly MiniMax M2.7 measured
IFBench75.7%75.7%Tie
Long context reasoning (AA-LCR)78.3%76.3%MiniMax M2.7
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B measured
ExploitBench13.3%Not measuredOnly MiniMax M2.7 measured
LMArena Text14051417Qwen3.5-122B-A10B
LMArena Text: instruction following14051399MiniMax M2.7
LMArena Text: creative writing13541368Qwen3.5-122B-A10B
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English14261428Qwen3.5-122B-A10B
LMArena Text: non-English13821401Qwen3.5-122B-A10B
LMArena Text: Chinese14421463Qwen3.5-122B-A10B
LMArena Text: German13981425Qwen3.5-122B-A10B
LMArena Text: Japanese12601367Qwen3.5-122B-A10B
LMArena Text: Korean13141353Qwen3.5-122B-A10B
LMArena Text: French14241443Qwen3.5-122B-A10B
LMArena Text: Spanish14021425Qwen3.5-122B-A10B
LMArena Text: Russian13831400Qwen3.5-122B-A10B
LMArena Text: Polish13871398Qwen3.5-122B-A10B
LMArena Text: multi-turn14121416Qwen3.5-122B-A10B
LMArena Text: longer queries14181410MiniMax M2.7
LMArena Text: writing and language13761387Qwen3.5-122B-A10B
LMArena Text: entertainment and media13601366Qwen3.5-122B-A10B
LMArena Vision: EnglishNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-122B-A10B measured
LMArena Vision: reading textNot measured1251Only Qwen3.5-122B-A10B measured
LMArena Vision: humorNot measured1243Only Qwen3.5-122B-A10B measured
LMArena Vision: creative writingNot measured1220Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 19 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 Qwen3.5-122B-A10B
BenchmarkMiniMax M2.7Qwen3.5-122B-A10BLeader
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
Qwen3.5-122B-A10B$0.0013Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

MiniMax M2.7$0.019Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

MiniMax M2.7$0.072Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits 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 Qwen3.5-122B-A10B
SpecificationMiniMax M2.7Qwen3.5-122B-A10B
MakerMiniMaxQwen
WeightsOpen weightsOpen weights
ReleasedMarch 18, 2026February 25, 2026
Context window205K262K
Input $/Mtok$0.30$0.26
Output $/Mtok$1.20$2.08
Coding index36.2Not benchmarked
Independent measurements behind it31
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is MiniMax M2.7 better than Qwen3.5-122B-A10B for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for a larger context window (262K against 205K tokens) and work measured by τ²-Bench Telecom or LMArena Text: math, where it leads.

Which is cheaper, MiniMax M2.7 or Qwen3.5-122B-A10B?

MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, MiniMax M2.7 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 205K.

How many benchmarks is the MiniMax M2.7 vs Qwen3.5-122B-A10B comparison based on?

40 benchmarks have a result for both models as of October 22, 2026. MiniMax M2.7's coding index rests on 3 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next