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GLM 4.5 vs Qwen3.8 27B

Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5 Replace Qwen3.8 27B
GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
6
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
31
vs boards won of 37 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 64.2 for GLM 4.5.

Pick GLM 4.5 when what matters is TaxEval (Vals AI) or LMArena Text: medicine and healthcare, where it leads.

Model AGLM 4.5Z.ai64.2/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
37
GLM 4.5 only
23
Qwen3.8 27B only
48
Like-for-like categories
6 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 4.5 64.2 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Intelligence, 20.9 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.5 52.7% against Qwen3.8 27B 82.0%, 29.3 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3.8 27B $1.35 per million output tokens, 1.6x apart.
Context window
GLM 4.5 takes 131K 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 4.5BarsQwen3.8 27BTies
Coding03
Intelligence01
Reasoning06
Math02
Knowledge25
General414
All shared boards631
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)67.5%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
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
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.5 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341483Qwen3.8 27B
LMArena Text: software and IT14401478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTMLNot measured1570Only Qwen3.8 27B measured
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 index01
Intelligence benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
AA Intelligence Index12.833.7Qwen3.8 27B
Epoch Capabilities IndexNot measured151.7Only Qwen3.8 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B 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 Telecom43.0%Not measuredOnly GLM 4.5 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
GPQA Diamond72.2%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)78.2%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)13.0%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 prompts14291459Qwen3.8 27B
LMArena Text: expert14331479Qwen3.8 27B
LMArena Text: hard prompts in English14291461Qwen3.8 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 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14271455Qwen3.8 27B
LMArena Text: mathematical work14301458Qwen3.8 27B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index25
Knowledge benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
MMLU Pro81.2%84.3%Qwen3.8 27B
LegalBench (Vals AI)75.6%82.4%Qwen3.8 27B
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%70.8%GLM 4.5
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
MMLU Pro (Artificial Analysis run)83.5%Not measuredOnly GLM 4.5 measured
LMArena Text: science14441467Qwen3.8 27B
LMArena Text: medicine and healthcare14551437GLM 4.5
LMArena Text: legal and government14381447Qwen3.8 27B
LMArena Text: business and finance14201438Qwen3.8 27B
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 27BLeader
Design Arena: websites1170Not measuredOnly GLM 4.5 measured
Design Arena: UI components1144Not measuredOnly GLM 4.5 measured
Design Arena: game dev1145Not measuredOnly GLM 4.5 measured
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index414
General benchmarks for GLM 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 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
IFBench44.1%Not measuredOnly GLM 4.5 measured
Long context reasoning (AA-LCR)52.7%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14301440Qwen3.8 27B
LMArena Text: instruction following14041436Qwen3.8 27B
LMArena Text: creative writing13951383GLM 4.5
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14371448Qwen3.8 27B
LMArena Text: non-English14171428Qwen3.8 27B
LMArena Text: Chinese14651501Qwen3.8 27B
LMArena Text: German14071443Qwen3.8 27B
LMArena Text: Japanese14151387GLM 4.5
LMArena Text: Korean13801384Qwen3.8 27B
LMArena Text: French14181465Qwen3.8 27B
LMArena Text: Spanish14541449GLM 4.5
LMArena Text: Russian14131415Qwen3.8 27B
LMArena Text: Polish14221439Qwen3.8 27B
LMArena Text: multi-turn14151440Qwen3.8 27B
LMArena Text: longer queries14121448Qwen3.8 27B
LMArena Text: writing and language14031405Qwen3.8 27B
LMArena Text: entertainment and media14041393GLM 4.5
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 4.5 and Qwen3.8 27B
BenchmarkGLM 4.5Qwen3.8 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 4.5$0.0017Fits in one request
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen3.8 27B$0.021Fits 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 4.5 and Qwen3.8 27B
SpecificationZ.ai GLM 4.5Qwen3.8 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025August 14, 2026
Context window131K1M
Input $/Mtok$0.60$0.04
Output $/Mtok$2.20$1.35
Coding index64.273.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 64.2 for GLM 4.5. Pick GLM 4.5 when what matters is TaxEval (Vals AI) or LMArena Text: medicine and healthcare, where it leads.

Which is cheaper, GLM 4.5 or Qwen3.8 27B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, GLM 4.5 or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3.8 27B comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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