Enter opens the highlighted result. Esc closes.

GLM 4.7 vs Qwen3.8 27B

Qwen3.8 27B leads GLM 4.7 on coding, 74.2 to 55.1 on the Vibe Coding Index, at about the same output price ($2.55 vs $2.20 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Replace GLM 4.7 Replace Qwen3.8 27B
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
11
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
35
vs boards won of 46 shared

GLM 4.7 or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 55.1 for GLM 4.7.

Pick GLM 4.7 for output tokens 1.2x cheaper ($2.20 vs $2.55 per million) and a lower cost per solved task ($0.29 against $1.26).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
46
GLM 4.7 only
35
Qwen3.8 27B only
39
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 4.7 55.1 from 2 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Coding, 15.4 points apart on average over 8 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 4.7 1420 against Qwen3.8 27B 1602, 30.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3.8 27B $2.55 per million output tokens, 1.2x apart.
Context window
GLM 4.7 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
CategoryGLM 4.7BarsQwen3.8 27BTies
Coding08
Intelligence02
Reasoning06
Math03
Knowledge35
General811
All shared boards1135
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
Coding benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev14351593Qwen3.8 27B
AA Coding Index45.368.1Qwen3.8 27B
LiveCodeBench (Vals AI)82.2%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)69.4%86.0%Qwen3.8 27B
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)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541483Qwen3.8 27B
LMArena Text: software and IT14531478Qwen3.8 27B
LMArena WebDev: React14201602Qwen3.8 27B
LMArena WebDev: HTML14471570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
AA Intelligence Index22.233.7Qwen3.8 27B
Epoch Capabilities Index143.6151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
Agentic work benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1113Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 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 Telecom95.9%Not measuredOnly GLM 4.7 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
GPQA Diamond80.0%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)85.9%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)27.4%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431459Qwen3.8 27B
LMArena Text: expert14241479Qwen3.8 27B
LMArena Text: hard prompts in English14521461Qwen3.8 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 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%16.0%Qwen3.8 27B
LMArena Text: math14241455Qwen3.8 27B
LMArena Text: mathematical work14111458Qwen3.8 27B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index35
Knowledge benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
MMLU Pro82.7%84.3%Qwen3.8 27B
LegalBench (Vals AI)83.4%82.4%GLM 4.7
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%70.8%Qwen3.8 27B
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%28.7%GLM 4.7
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571467Qwen3.8 27B
LMArena Text: medicine and healthcare14691437GLM 4.7
LMArena Text: legal and government14441447Qwen3.8 27B
LMArena Text: business and finance14291438Qwen3.8 27B
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 27BLeader
Design Arena: websites1225Not measuredOnly GLM 4.7 measured
Design Arena: UI components1197Not measuredOnly GLM 4.7 measured
Design Arena: game dev1188Not measuredOnly GLM 4.7 measured
Design Arena: data viz1196Not measuredOnly GLM 4.7 measured
Design Arena: 3D1190Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1216Not measuredOnly GLM 4.7 measured
Design Arena: SVG1142Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index811
General benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 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)68.6%83.8%Qwen3.8 27B
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351440Qwen3.8 27B
LMArena Text: instruction following14121436Qwen3.8 27B
LMArena Text: creative writing14011383GLM 4.7
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14531448GLM 4.7
LMArena Text: non-English14171428Qwen3.8 27B
LMArena Text: Chinese14951501Qwen3.8 27B
LMArena Text: German14261443Qwen3.8 27B
LMArena Text: Japanese14391387GLM 4.7
LMArena Text: Korean13991384GLM 4.7
LMArena Text: French14321465Qwen3.8 27B
LMArena Text: Spanish14341449Qwen3.8 27B
LMArena Text: Russian14231415GLM 4.7
LMArena Text: Polish14351439Qwen3.8 27B
LMArena Text: multi-turn14461440GLM 4.7
LMArena Text: longer queries14321448Qwen3.8 27B
LMArena Text: writing and language14111405GLM 4.7
LMArena Text: entertainment and media14001393GLM 4.7
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 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Qwen3.8 27B
BenchmarkGLM 4.7Qwen3.8 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

GLM 4.7$0.0017Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Same cost

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds 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 4.7 and Qwen3.8 27B
SpecificationZ.ai GLM 4.7Qwen3.8 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025August 14, 2026
Context window205K1M
Input $/Mtok$0.60$0.425
Output $/Mtok$2.20$2.55
Coding index55.174.2
Independent measurements behind it24
Cost per solved task$0.29$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for output tokens 1.2x cheaper ($2.20 vs $2.55 per million) and a lower cost per solved task ($0.29 against $1.26).

Which is cheaper, GLM 4.7 or Qwen3.8 27B?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Qwen3.8 27B $1.26.

Which has the bigger context window, GLM 4.7 or Qwen3.8 27B?

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

How many benchmarks is the GLM 4.7 vs Qwen3.8 27B comparison based on?

46 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/glm-4-7-vs-qwen3-8-27b" title="GLM 4.7 vs Qwen3.8 27B, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-7-vs-qwen3-8-27b">LLMBenchmark.ai, GLM 4.7 vs Qwen3.8 27B</a></p>