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GLM 5 vs Qwen3.8 27B

Qwen3.8 27B leads GLM 5 on coding, 74.2 to 41.7 on the Vibe Coding Index, and Qwen3.8 27B costs 1.3x less per output token ($2.55 vs $3.20 per million). Updated October 22, 2026; 45 benchmarks have a result for both.

Replace GLM 5 Replace Qwen3.8 27B
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
17
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
28
vs boards won of 45 shared

GLM 5 or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 41.7 for GLM 5.

Pick GLM 5 for a lower cost per solved task ($0.63 against $1.26) and work measured by MMLU Pro or LegalBench (Vals AI), where it leads.

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
45
GLM 5 only
31
Qwen3.8 27B only
40
Like-for-like categories
6 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, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Coding, 17.1 points apart on average over 8 shared benchmarks.
Widest single gap
ARC-AGI: GLM 5 44.7% against Qwen3.8 27B 87.5%, 42.8 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against Qwen3.8 27B $2.55 per million output tokens, 1.3x apart.
Context window
GLM 5 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
CategoryGLM 5BarsQwen3.8 27BTies
Coding08
Intelligence02
Reasoning08
Math02
Knowledge43
General135
All shared boards1728
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
Coding benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev14351593Qwen3.8 27B
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)81.9%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)71.4%86.0%Qwen3.8 27B
Vibe Code Bench (Vals AI)23.4%64.8%Qwen3.8 27B
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)43.2%Not measuredOnly GLM 5 measured
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
WeirdML48.2%Not measuredOnly GLM 5 measured
ALE-Bench765.6Not measuredOnly GLM 5 measured
LMArena Text: coding14611483Qwen3.8 27B
LMArena Text: software and IT14601478Qwen3.8 27B
LMArena WebDev: React14241602Qwen3.8 27B
LMArena WebDev: HTML14501570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
AA Intelligence Index27.933.7Qwen3.8 27B
Epoch Capabilities Index145.8151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
Agentic work benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 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 apps1147Not measuredOnly GLM 5 measured
Design Arena: agents building Godot games1144Not measuredOnly GLM 5 measured
Design Arena: HTML slides1098Not measuredOnly GLM 5 measured
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)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom98.2%Not measuredOnly GLM 5 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Vending-Bench 2$4,432Not measuredOnly GLM 5 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
Reasoning benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
GPQA Diamond83.3%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)82.0%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)29.3%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)87.8%Not measuredOnly GLM 5 measured
ARC-AGI44.7%87.5%Qwen3.8 27B
ARC-AGI-24.9%42.4%Qwen3.8 27B
SimpleBench53.2%Not measuredOnly GLM 5 measured
LMArena Text: hard prompts14521459Qwen3.8 27B
LMArena Text: expert14541479Qwen3.8 27B
LMArena Text: hard prompts in English14601461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Kagi LLM Benchmark75.0%Not measuredOnly GLM 5 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%Not measuredOnly GLM 5 measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14401455Qwen3.8 27B
LMArena Text: mathematical work14511458Qwen3.8 27B
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index43
Knowledge benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
MMLU Pro86.0%84.3%GLM 5
LegalBench (Vals AI)84.1%82.4%GLM 5
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%70.8%Qwen3.8 27B
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
LMArena Text: science14601467Qwen3.8 27B
LMArena Text: medicine and healthcare14551437GLM 5
LMArena Text: legal and government14531447GLM 5
LMArena Text: business and finance14301438Qwen3.8 27B
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 27BLeader
Design Arena: websites1247Not measuredOnly GLM 5 measured
Design Arena: UI components1227Not measuredOnly GLM 5 measured
Design Arena: game dev1234Not measuredOnly GLM 5 measured
Design Arena: data viz1228Not measuredOnly GLM 5 measured
Design Arena: 3D1228Not 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
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index135
General benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 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)Not measured83.8%Only Qwen3.8 27B measured
IFBench72.3%Not measuredOnly GLM 5 measured
Long context reasoning (AA-LCR)75.7%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
CL-bench18.7%Not measuredOnly GLM 5 measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14461440GLM 5
LMArena Text: instruction following14281436Qwen3.8 27B
LMArena Text: creative writing14381383GLM 5
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14581448GLM 5
LMArena Text: non-English14301428GLM 5
LMArena Text: Chinese15111501GLM 5
LMArena Text: German14471443GLM 5
LMArena Text: Japanese14161387GLM 5
LMArena Text: Korean14231384GLM 5
LMArena Text: French14561465Qwen3.8 27B
LMArena Text: Spanish14541449GLM 5
LMArena Text: Russian14361415GLM 5
LMArena Text: Polish14341439Qwen3.8 27B
LMArena Text: multi-turn14561440GLM 5
LMArena Text: longer queries14461448Qwen3.8 27B
LMArena Text: writing and language14371405GLM 5
LMArena Text: entertainment and media14261393GLM 5
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 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and Qwen3.8 27B
BenchmarkGLM 5Qwen3.8 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

GLM 5$0.0026Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 5$0.232Exceeds the context window
Qwen3.8 27B$0.111Fits in one request

Qwen3.8 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 Qwen3.8 27B
SpecificationZ.ai GLM 5Qwen3.8 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 11, 2026August 14, 2026
Context window205K1M
Input $/Mtok$1.00$0.425
Output $/Mtok$3.20$2.55
Coding index41.774.2
Independent measurements behind it34
Cost per solved task$0.63$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 41.7 for GLM 5. Pick GLM 5 for a lower cost per solved task ($0.63 against $1.26) and work measured by MMLU Pro or LegalBench (Vals AI), where it leads.

Which is cheaper, GLM 5 or Qwen3.8 27B?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and Qwen3.8 27B $1.26.

Which has the bigger context window, GLM 5 or Qwen3.8 27B?

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

How many benchmarks is the GLM 5 vs Qwen3.8 27B comparison based on?

45 benchmarks have a result for both models as of October 22, 2026. GLM 5'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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