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GLM 5.2 vs Qwen3.7 Plus

GLM 5.2 leads Qwen3.7 Plus on coding, 61.1 to 33.2 on the Vibe Coding Index, and Qwen3.7 Plus costs 3.4x less per output token ($1.28 vs $4.40 per million). Updated October 21, 2026; 61 benchmarks have a result for both.

Replace GLM 5.2 Replace Qwen3.7 Plus
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
56
Qwen3.7 PlusQwen
Coding
33.2
Overall
54.0
Output $/M
$1.28
5
vs boards won of 61 shared

GLM 5.2 or Qwen3.7 Plus: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.

Pick Qwen3.7 Plus for output tokens 3.4x cheaper ($1.28 vs $4.40 per million) and work measured by SkillsBench (Vals AI) or GPQA Diamond (Artificial Analysis run), where it leads.

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
61
GLM 5.2 only
37
Qwen3.7 Plus only
14
Like-for-like categories
8 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 5.2 61.1 from 4 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Agentic work, 13.1 points apart on average over 5 shared benchmarks.
Widest single gap
Code migration (Vals AI): GLM 5.2 37.9% against Qwen3.7 Plus 12.9%, 25.0 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against Qwen3.7 Plus $1.28 per million output tokens, 3.4x apart.
Context window
GLM 5.2 takes 1.05M tokens, Qwen3.7 Plus 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 5.2BarsQwen3.7 PlusTies
Coding91
Intelligence20
Agentic work50
Reasoning61
Math31
Knowledge40
Web design80
General192
All shared boards565
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
Coding benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
LMArena WebDev1604Not measuredOnly GLM 5.2 measured
AA Coding Index68.855.9GLM 5.2
LiveCodeBench (Vals AI)69.5%Not measuredOnly GLM 5.2 measured
SWE-bench Verified (Vals AI)82.8%Not measuredOnly GLM 5.2 measured
Vibe Code Bench (Vals AI)64.0%46.4%GLM 5.2
Code migration (Vals AI)37.9%12.9%GLM 5.2
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%52.8%GLM 5.2
SkillsBench (Vals AI)45.1%54.3%Qwen3.7 Plus
SciCode51.2%46.1%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%47.0%GLM 5.2
FrontierCode24.5%10.2%GLM 5.2
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0Not measuredOnly GLM 5.2 measured
LMArena Text: coding14871473GLM 5.2
LMArena Text: software and IT14861473GLM 5.2
LMArena WebDev: React1617Not measuredOnly GLM 5.2 measured
LMArena WebDev: HTML1565Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
AA Intelligence Index33.725.2GLM 5.2
Epoch Capabilities Index151.9147.0GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
AA Agentic Index38.417.5GLM 5.2
Design Arena: agents building web apps1222Not measuredOnly GLM 5.2 measured
Design Arena: agents building full-stack apps1241Not measuredOnly GLM 5.2 measured
Design Arena: agents building mobile apps1160Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1178Not measuredOnly GLM 5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games1206Not measuredOnly GLM 5.2 measured
Design Arena: HTML slides1175Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1195Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%38.2%GLM 5.2
Tax Agent Bench (Vals AI)Not measured10.5%Only Qwen3.7 Plus measured
Legal Research Bench (Vals AI)31.3%16.4%GLM 5.2
Excel Modeling Benchmark (Vals AI)61.5%49.3%GLM 5.2
τ²-Bench Telecom99.1%93.0%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index61
Reasoning benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
GPQA Diamond85.6%Not measuredOnly GLM 5.2 measured
GPQA Diamond (Artificial Analysis run)89.5%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)41.1%35.6%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%87.9%GLM 5.2
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%Not measuredOnly GLM 5.2 measured
Mystery game puzzles19.0%17.0%GLM 5.2
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811460GLM 5.2
LMArena Text: expert14871466GLM 5.2
LMArena Text: hard prompts in English14811460GLM 5.2
LMArena Vision: diagramsNot measured1292Only Qwen3.7 Plus measured
LMArena Vision: homeworkNot measured1295Only Qwen3.7 Plus measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index31
Math benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
OTIS Mock AIME86.4%93.3%Qwen3.7 Plus
FrontierMath, tiers 1 to 359.2%34.4%GLM 5.2
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%Not measuredOnly GLM 5.2 measured
LMArena Text: math14831467GLM 5.2
LMArena Text: mathematical work14891470GLM 5.2
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 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
MMLU Pro86.7%Not measuredOnly GLM 5.2 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5.2 measured
CorpFin (Vals AI)66.1%Not measuredOnly GLM 5.2 measured
TaxEval (Vals AI)73.3%Not measuredOnly GLM 5.2 measured
MortgageTax (Vals AI)Not measured66.2%Only Qwen3.7 Plus measured
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891464GLM 5.2
LMArena Text: medicine and healthcare14751464GLM 5.2
LMArena Text: legal and government14861451GLM 5.2
LMArena Text: business and finance14511435GLM 5.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
Design Arena: websites12851269GLM 5.2
Design Arena: UI components12961261GLM 5.2
Design Arena: game dev12681262GLM 5.2
Design Arena: data viz12851276GLM 5.2
Design Arena: 3D12881239GLM 5.2
Design Arena: all code categories12881269GLM 5.2
Design Arena: SVG12141208GLM 5.2
Design Arena: ASCII art11901143GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index192
General benchmarks for GLM 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
SAGE (Vals AI)Not measured39.3%Only Qwen3.7 Plus measured
MedScribe (Vals AI)83.5%Not measuredOnly GLM 5.2 measured
Vals Multimodal IndexNot measured53.9%Only Qwen3.7 Plus measured
IFBench73.3%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)78.3%73.0%GLM 5.2
DTBench93.6%84.0%GLM 5.2
LMCA45.8%37.6%GLM 5.2
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
LMArena Text14701454GLM 5.2
LMArena Text: instruction following14661439GLM 5.2
LMArena Text: creative writing14631438GLM 5.2
LMArena VisionNot measured1278Only Qwen3.7 Plus measured
LMArena Text: English14751458GLM 5.2
LMArena Text: non-English14591445GLM 5.2
LMArena Text: Chinese15221509GLM 5.2
LMArena Text: German14701471Qwen3.7 Plus
LMArena Text: Japanese14501412GLM 5.2
LMArena Text: Korean14401413GLM 5.2
LMArena Text: French14791472GLM 5.2
LMArena Text: Spanish14781457GLM 5.2
LMArena Text: Russian14681457GLM 5.2
LMArena Text: Polish14561449GLM 5.2
LMArena Text: multi-turn14691459GLM 5.2
LMArena Text: longer queries14801454GLM 5.2
LMArena Text: writing and language14631437GLM 5.2
LMArena Text: entertainment and media14501422GLM 5.2
LMArena Vision: EnglishNot measured1280Only Qwen3.7 Plus measured
LMArena Vision: ChineseNot measured1332Only Qwen3.7 Plus measured
LMArena Vision: reading textNot measured1288Only Qwen3.7 Plus measured
LMArena Vision: humorNot measured1261Only Qwen3.7 Plus measured
LMArena Vision: creative writingNot measured1258Only Qwen3.7 Plus measured
LMArena DocumentNot measured1444Only Qwen3.7 Plus 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 5.2 and Qwen3.7 Plus
BenchmarkGLM 5.2Qwen3.7 PlusLeader
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 5.2$0.0036Fits in one request
Qwen3.7 Plus$0.0010Fits in one request

Qwen3.7 Plus costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.7 Plus costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
Qwen3.7 Plus$0.077Fits in one request

Qwen3.7 Plus 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 5.2 and Qwen3.7 Plus
SpecificationZ.ai GLM 5.2Qwen3.7 Plus
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedJune 16, 2026June 3, 2026
Context window1.05M1M
Input $/Mtok$1.40$0.32
Output $/Mtok$4.40$1.28
Coding index61.133.2
Independent measurements behind it42
Cost per solved task$0.86Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Qwen3.7 Plus for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus for output tokens 3.4x cheaper ($1.28 vs $4.40 per million) and work measured by SkillsBench (Vals AI) or GPQA Diamond (Artificial Analysis run), where it leads.

Which is cheaper, GLM 5.2 or Qwen3.7 Plus?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, GLM 5.2 or Qwen3.7 Plus?

GLM 5.2, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.2 vs Qwen3.7 Plus comparison based on?

61 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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