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GLM 5 vs Qwen3.6 27B

GLM 5 leads Qwen3.6 27B on coding, 41.7 to 36.8 on the Vibe Coding Index, at about the same output price ($3.20 vs $2.70 per million). Updated October 21, 2026; 15 benchmarks have a result for both.

Replace GLM 5 Replace Qwen3.6 27B
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
9
Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
6
vs boards won of 15 shared

GLM 5 or Qwen3.6 27B: which to pick

Pick GLM 5 for coding: it scores 41.7 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.

Pick Qwen3.6 27B for output tokens 1.2x cheaper ($2.70 vs $3.20 per million) and a larger context window (262K against 205K tokens).

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BQwen3.6 27BQwen36.8/1002 independent benchmarks$1.30 per solved task
Shared results
15
GLM 5 only
61
Qwen3.6 27B only
9
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
GLM 5 41.7 from 3 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
Widest category
Math, 11.1 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5 23.4% against Qwen3.6 27B 11.9%, 11.4 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against Qwen3.6 27B $2.70 per million output tokens, 1.2x apart.
Context window
GLM 5 takes 205K tokens, Qwen3.6 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 5BarsQwen3.6 27BTies
Coding30
Intelligence11
Agentic work10
Reasoning21
Math01
Knowledge12
General11
All shared boards96
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LMArena WebDev1435Not measuredOnly GLM 5 measured
AA Coding IndexNot measured53.7Only Qwen3.6 27B measured
LiveCodeBench (Vals AI)81.9%Not measuredOnly GLM 5 measured
SWE-bench Verified (Vals AI)71.4%70.0%GLM 5
Vibe Code Bench (Vals AI)23.4%11.9%GLM 5
SciCodeNot measured42.8%Only Qwen3.6 27B measured
Terminal-Bench Hard (Artificial Analysis run)43.2%34.8%GLM 5
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
WeirdML48.2%Not measuredOnly GLM 5 measured
ALE-Bench765.6Not measuredOnly GLM 5 measured
LMArena Text: coding1461Not measuredOnly GLM 5 measured
LMArena Text: software and IT1460Not measuredOnly GLM 5 measured
LMArena WebDev: React1424Not measuredOnly GLM 5 measured
LMArena WebDev: HTML1450Not measuredOnly GLM 5 measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
AA Intelligence Index27.921.4GLM 5
Epoch Capabilities Index145.8146.2Qwen3.6 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
AA Agentic IndexNot measured18.5Only Qwen3.6 27B measured
Design Arena: agents building full-stack apps1123Not measuredOnly GLM 5 measured
Design Arena: agents building mobile apps1136Not measuredOnly GLM 5 measured
Design Arena: agents building Android apps1146Not measuredOnly GLM 5 measured
Design Arena: agents building Godot games1144Not measuredOnly GLM 5 measured
Design Arena: HTML slides1148Not measuredOnly GLM 5 measured
τ²-Bench Telecom98.2%94.2%GLM 5
Vending-Bench 2$4,432Not measuredOnly GLM 5 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
Reasoning benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
GPQA Diamond83.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Artificial Analysis run)82.0%84.2%Qwen3.6 27B
Humanity's Last Exam (Artificial Analysis run)29.3%23.1%GLM 5
GPQA Diamond (Epoch AI run)87.8%85.9%GLM 5
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%Not measuredOnly GLM 5 measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
LMArena Text: hard prompts1452Not measuredOnly GLM 5 measured
LMArena Text: expert1454Not measuredOnly GLM 5 measured
LMArena Text: hard prompts in English1460Not measuredOnly GLM 5 measured
Kagi LLM Benchmark75.0%Not measuredOnly GLM 5 measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%91.1%Qwen3.6 27B
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B measured
LMArena Text: math1440Not measuredOnly GLM 5 measured
LMArena Text: mathematical work1451Not measuredOnly GLM 5 measured
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12
Knowledge benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
MMLU Pro86.0%Not measuredOnly GLM 5 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5 measured
CaseLaw (Vals AI)52.5%53.2%Qwen3.6 27B
CorpFin (Vals AI)62.9%62.3%GLM 5
TaxEval (Vals AI)70.0%71.3%Qwen3.6 27B
MortgageTax (Vals AI)Not measured68.3%Only Qwen3.6 27B measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
LMArena Text: science1460Not measuredOnly GLM 5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 5 measured
LMArena Text: legal and government1453Not measuredOnly GLM 5 measured
LMArena Text: business and finance1430Not measuredOnly GLM 5 measured
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
Design Arena: websites1247Not measuredOnly GLM 5 measured
Design Arena: UI components1230Not measuredOnly GLM 5 measured
Design Arena: game dev1234Not measuredOnly GLM 5 measured
Design Arena: data viz1228Not measuredOnly GLM 5 measured
Design Arena: 3D1229Not measuredOnly GLM 5 measured
Design Arena: all code categories1246Not measuredOnly GLM 5 measured
Design Arena: SVG1168Not measuredOnly GLM 5 measured
Design Arena: ASCII art1147Not measuredOnly GLM 5 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 GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 27BLeader
SAGE (Vals AI)Not measured45.6%Only Qwen3.6 27B measured
IFBench72.3%67.6%GLM 5
Long context reasoning (AA-LCR)75.7%77.3%Qwen3.6 27B
DTBenchNot measured78.1%Only Qwen3.6 27B measured
LMCANot measured34.5%Only Qwen3.6 27B measured
CL-bench18.7%Not measuredOnly GLM 5 measured
LMArena Text1446Not measuredOnly GLM 5 measured
LMArena Text: instruction following1428Not measuredOnly GLM 5 measured
LMArena Text: creative writing1438Not measuredOnly GLM 5 measured
LMArena Text: English1458Not measuredOnly GLM 5 measured
LMArena Text: non-English1430Not measuredOnly GLM 5 measured
LMArena Text: Chinese1511Not measuredOnly GLM 5 measured
LMArena Text: German1447Not measuredOnly GLM 5 measured
LMArena Text: Japanese1416Not measuredOnly GLM 5 measured
LMArena Text: Korean1423Not measuredOnly GLM 5 measured
LMArena Text: French1456Not measuredOnly GLM 5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 5 measured
LMArena Text: Russian1436Not measuredOnly GLM 5 measured
LMArena Text: Polish1434Not measuredOnly GLM 5 measured
LMArena Text: multi-turn1456Not measuredOnly GLM 5 measured
LMArena Text: longer queries1446Not measuredOnly GLM 5 measured
LMArena Text: writing and language1437Not measuredOnly GLM 5 measured
LMArena Text: entertainment and media1426Not measuredOnly GLM 5 measured
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and Qwen3.6 27B
BenchmarkGLM 5Qwen3.6 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 5$0.0026Fits in one request
Qwen3.6 27B$0.0018Fits in one request

Qwen3.6 27B costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits in one request
Qwen3.6 27B$0.031Fits in one request

Qwen3.6 27B costs less

Agent loop

200K input + 10K output tokens

GLM 5$0.232Exceeds the context window
Qwen3.6 27B$0.117Fits in one request

Qwen3.6 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 5 and Qwen3.6 27B
SpecificationZ.ai GLM 5Qwen3.6 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 11, 2026April 27, 2026
Context window205K262K
Input $/Mtok$1.00$0.45
Output $/Mtok$3.20$2.70
Coding index41.736.8
Independent measurements behind it32
Cost per solved task$0.63$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 better than Qwen3.6 27B for coding?

Pick GLM 5 for coding: it scores 41.7 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Pick Qwen3.6 27B for output tokens 1.2x cheaper ($2.70 vs $3.20 per million) and a larger context window (262K against 205K tokens).

Which is cheaper, GLM 5 or Qwen3.6 27B?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and Qwen3.6 27B $1.30.

Which has the bigger context window, GLM 5 or Qwen3.6 27B?

Qwen3.6 27B, with 262K tokens against 205K.

How many benchmarks is the GLM 5 vs Qwen3.6 27B comparison based on?

15 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-vs-qwen3-6-27b">LLMBenchmark.ai, GLM 5 vs Qwen3.6 27B</a></p>