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MiniMax M2.7 vs Qwen3.8 27B

Qwen3.8 27B leads MiniMax M2.7 on coding, 74.2 to 36.2 on the Vibe Coding Index, and MiniMax M2.7 costs 2.1x less per output token ($1.20 vs $2.55 per million). Updated October 22, 2026; 56 benchmarks have a result for both.

Replace MiniMax M2.7 Replace Qwen3.8 27B
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
3
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
52
vs boards won of 56 shared, 1 tied

MiniMax M2.7 or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 36.2 for MiniMax M2.7.

Pick MiniMax M2.7 for output tokens 2.1x cheaper ($1.20 vs $2.55 per million) and a lower cost per solved task ($0.64 against $1.26).

Model AMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
56
MiniMax M2.7 only
18
Qwen3.8 27B only
29
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, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Agentic work, 26.4 points apart on average over 5 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): MiniMax M2.7 11.9% against Qwen3.8 27B 64.8%, 52.9 points apart on a 0 to 100 scale.
Output price
MiniMax M2.7 $1.20 against Qwen3.8 27B $2.55 per million output tokens, 2.1x apart.
Context window
MiniMax M2.7 takes 205K tokens, Qwen3.8 27B 1M.

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 M2.7BarsQwen3.8 27BTies
Coding1111 tie
Intelligence02
Agentic work05
Reasoning06
Math03
Knowledge26
General019
All shared boards3521 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index111
Coding benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev13991593Qwen3.8 27B
AA Coding Index52.668.1Qwen3.8 27B
LiveCodeBench (Vals AI)79.9%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)73.8%86.0%Qwen3.8 27B
Vibe Code Bench (Vals AI)11.9%64.8%Qwen3.8 27B
Code migration (Vals AI)8.7%14.2%Qwen3.8 27B
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)48.7%58.4%Qwen3.8 27B
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
SciCode50.1%46.6%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)39.4%Not measuredOnly MiniMax M2.7 measured
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: coding14541483Qwen3.8 27B
LMArena Text: software and IT14461478Qwen3.8 27B
LMArena WebDev: React13881602Qwen3.8 27B
LMArena WebDev: HTML14011570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
AA Intelligence Index22.833.7Qwen3.8 27B
Epoch Capabilities Index145.8151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index05
Agentic work benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
AA Agentic Index15.345.8Qwen3.8 27B
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)27.9%48.5%Qwen3.8 27B
Tax Agent Bench (Vals AI)6.9%30.9%Qwen3.8 27B
Legal Research Bench (Vals AI)10.6%36.1%Qwen3.8 27B
Excel Modeling Benchmark (Vals AI)28.4%59.7%Qwen3.8 27B
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom84.8%Not measuredOnly MiniMax M2.7 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
GPQA Diamond86.6%88.9%Qwen3.8 27B
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)87.4%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)29.6%33.9%Qwen3.8 27B
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts14221459Qwen3.8 27B
LMArena Text: expert14431479Qwen3.8 27B
LMArena Text: hard prompts in English14411461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
AIME91.0%Not measuredOnly MiniMax M2.7 measured
ProofBench3.0%16.0%Qwen3.8 27B
LMArena Text: math14211455Qwen3.8 27B
LMArena Text: mathematical work14371458Qwen3.8 27B
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index26
Knowledge benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
MMLU Pro80.4%84.3%Qwen3.8 27B
LegalBench (Vals AI)84.0%82.4%MiniMax M2.7
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%70.8%Qwen3.8 27B
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)34.4%28.7%MiniMax M2.7
LMArena Text: science14161467Qwen3.8 27B
LMArena Text: medicine and healthcare14091437Qwen3.8 27B
LMArena Text: legal and government14031447Qwen3.8 27B
LMArena Text: business and finance14071438Qwen3.8 27B
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
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
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for MiniMax M2.7 and Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)79.9%83.8%Qwen3.8 27B
IFBench75.7%Not measuredOnly MiniMax M2.7 measured
Long context reasoning (AA-LCR)78.3%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
ExploitBench13.3%Not measuredOnly MiniMax M2.7 measured
LMArena Text14051440Qwen3.8 27B
LMArena Text: instruction following14051436Qwen3.8 27B
LMArena Text: creative writing13541383Qwen3.8 27B
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14261448Qwen3.8 27B
LMArena Text: non-English13821428Qwen3.8 27B
LMArena Text: Chinese14421501Qwen3.8 27B
LMArena Text: German13981443Qwen3.8 27B
LMArena Text: Japanese12601387Qwen3.8 27B
LMArena Text: Korean13141384Qwen3.8 27B
LMArena Text: French14241465Qwen3.8 27B
LMArena Text: Spanish14021449Qwen3.8 27B
LMArena Text: Russian13831415Qwen3.8 27B
LMArena Text: Polish13871439Qwen3.8 27B
LMArena Text: multi-turn14121440Qwen3.8 27B
LMArena Text: longer queries14181448Qwen3.8 27B
LMArena Text: writing and language13761405Qwen3.8 27B
LMArena Text: entertainment and media13601393Qwen3.8 27B
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 16 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 Qwen3.8 27B
BenchmarkMiniMax M2.7Qwen3.8 27BLeader
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
Qwen3.8 27B$0.0017Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

MiniMax M2.7$0.019Fits in one request
Qwen3.8 27B$0.029Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

MiniMax M2.7$0.072Exceeds the context window
Qwen3.8 27B$0.111Fits 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 Qwen3.8 27B
SpecificationMiniMax M2.7Qwen3.8 27B
MakerMiniMaxQwen
WeightsOpen weightsOpen weights
ReleasedMarch 18, 2026August 14, 2026
Context window205K1M
Input $/Mtok$0.30$0.425
Output $/Mtok$1.20$2.55
Coding index36.274.2
Independent measurements behind it34
Cost per solved task$0.64$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is MiniMax M2.7 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 36.2 for MiniMax M2.7. Pick MiniMax M2.7 for output tokens 2.1x cheaper ($1.20 vs $2.55 per million) and a lower cost per solved task ($0.64 against $1.26).

Which is cheaper, MiniMax M2.7 or Qwen3.8 27B?

MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, MiniMax M2.7 costs $0.64 and Qwen3.8 27B $1.26.

Which has the bigger context window, MiniMax M2.7 or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 205K.

How many benchmarks is the MiniMax M2.7 vs Qwen3.8 27B comparison based on?

56 benchmarks have a result for both models as of October 22, 2026. MiniMax M2.7's coding index rests on 3 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

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