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GLM 4.6 vs Qwen3.5-27B

Updated October 21, 2026. 38 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace Qwen3.5-27B
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
28
Qwen3.5-27BQwen
Coding
22.6
Overall
22.9
Output $/M
$2.16
10
vs boards won of 38 shared

Decision reading

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.

Pick Qwen3.5-27B when output tokens cost 1.0x less, or when the context window is larger, 262K against 205K tokens.

Model AGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved taskModel BQwen3.5-27BQwen22.6/1001 independent benchmarkNo cost per solved task
Shared results
38
GLM 4.6 only
28
Qwen3.5-27B only
15
Like-for-like categories
7 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
GLM 4.6 52.2 from 2 benchmarks, Qwen3.5-27B 22.6 from 1 benchmark.
Widest category
Agentic work, 17.0 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.6 43.4% against Qwen3.5-27B 75.6%, 32.2 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Qwen3.5-27B $2.16 per million output tokens, 1.0x apart.
Context window
GLM 4.6 takes 205K tokens, Qwen3.5-27B 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
CategoryGLM 4.6BarsQwen3.5-27BTies
Coding24
Intelligence01
Agentic work01
Reasoning32
Math20
Knowledge40
General172
All shared boards2810
CodingLike-for-likeFour of these boards feed the Vibe Coding Index24
Coding benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411358Qwen3.5-27B
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%32.6%Qwen3.5-27B
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
WeirdMLNot measured39.5%Only Qwen3.5-27B measured
ALE-Bench340.8349.4Qwen3.5-27B
LMArena Text: coding14491427GLM 4.6
LMArena Text: software and IT14551431GLM 4.6
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTML13501386Qwen3.5-27B
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
AA Intelligence Index18.522.9Qwen3.5-27B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1090Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%93.9%Qwen3.5-27B
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
Reasoning benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)14.5%23.9%Qwen3.5-27B
LMArena Text: hard prompts14401415GLM 4.6
LMArena Text: expert14321427GLM 4.6
LMArena Text: hard prompts in English14451429GLM 4.6
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math14321429GLM 4.6
LMArena Text: mathematical work14471429GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561437GLM 4.6
LMArena Text: medicine and healthcare14551425GLM 4.6
LMArena Text: legal and government14481405GLM 4.6
LMArena Text: business and finance14161399GLM 4.6
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1147Not measuredOnly GLM 4.6 measured
Design Arena: game dev1151Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1129Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1166Not measuredOnly GLM 4.6 measured
Design Arena: SVG1108Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
General benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
IFBench43.4%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)54.0%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
LMArena Text14401409GLM 4.6
LMArena Text: instruction following14101393GLM 4.6
LMArena Text: creative writing14121362GLM 4.6
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English14491425GLM 4.6
LMArena Text: non-English14261390GLM 4.6
LMArena Text: Chinese14991478GLM 4.6
LMArena Text: German14471392GLM 4.6
LMArena Text: Japanese13931346GLM 4.6
LMArena Text: Korean14001359GLM 4.6
LMArena Text: French14591410GLM 4.6
LMArena Text: Spanish14361407GLM 4.6
LMArena Text: Russian14181390GLM 4.6
LMArena Text: Polish14471375GLM 4.6
LMArena Text: multi-turn14271410GLM 4.6
LMArena Text: longer queries14221413GLM 4.6
LMArena Text: writing and language14151380GLM 4.6
LMArena Text: entertainment and media14091363GLM 4.6
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Qwen3.5-27B
BenchmarkGLM 4.6Qwen3.5-27BLeader
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

GLM 4.6$0.0016Fits in one request
Qwen3.5-27B$0.0014Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.034Fits in one request
Qwen3.5-27B$0.020Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.132Exceeds the context window
Qwen3.5-27B$0.076Fits in one request

Qwen3.5-27B 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 GLM 4.6 and Qwen3.5-27B
SpecificationZ.ai GLM 4.6Qwen3.5-27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025February 25, 2026
Context window205K262K
Input $/Mtok$0.55$0.27
Output $/Mtok$2.20$2.16
Coding index52.222.6
Independent measurements behind it21
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Qwen3.5-27B for coding?

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when output tokens cost 1.0x less, or when the context window is larger, 262K against 205K tokens.

Which is cheaper, GLM 4.6 or Qwen3.5-27B?

GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Qwen3.5-27B comparison based on?

38 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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