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GLM 5.2 vs Step 5 Preview

Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GLM 5.2Z.ai
Coding
57.7
Overall
33.7
Output $/M
$7.00
29
Step 5 PreviewStepFun
Coding
71.4
Overall
43.7
Output $/M
$2.70
10
vs boards won of 39 shared

Decision reading

Pick Step 5 Preview for coding: it scores 71.4 on the Vibe Coding Index against 57.7 for GLM 5.2.

Pick GLM 5.2 when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Shared results
39
GLM 5.2 only
58
Step 5 Preview only
22
Like-for-like categories
7 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 5.2 57.7 from 1 benchmark, Step 5 Preview 71.4 from 2 benchmarks.
Widest category
Intelligence, 10.0 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Japanese: GLM 5.2 1450 against Step 5 Preview 1383, 11.1 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $7.00 against Step 5 Preview $2.70 per million output tokens, 2.6x apart.
Context window
Both take 1.05M tokens.

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 5.2BarsStep 5 PreviewTies
Coding53
Intelligence01
Agentic work13
Reasoning31
Math21
Knowledge40
General141
All shared boards2910
CodingLike-for-likeFour of these boards feed the Vibe Coding Index53
Coding benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
LiveBench codingNot measured85.6%Only Step 5 Preview measured
LMArena WebDev16041567GLM 5.2
AA Coding Index68.8Not measuredOnly GLM 5.2 measured
LiveCodeBench (Vals AI)69.5%Not measuredOnly GLM 5.2 measured
SWE-bench Verified (Vals AI)82.8%Not measuredOnly GLM 5.2 measured
Terminal-Bench 4 (Vals AI)Not measured31.8%Only Step 5 Preview measured
Vibe Code Bench (Vals AI)64.0%70.4%Step 5 Preview
Code migration (Vals AI)37.9%39.0%Step 5 Preview
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
IOI (Vals AI)Not measured38.1%Only Step 5 Preview measured
Terminal-Bench 2.1 (Vals AI)67.8%Not measuredOnly GLM 5.2 measured
SWE-bench Multimodal (Vals AI)Not measured40.8%Only Step 5 Preview measured
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
SciCode51.2%58.9%Step 5 Preview
Terminal-Bench Hard (Artificial Analysis run)50.8%Not measuredOnly GLM 5.2 measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0Not measuredOnly GLM 5.2 measured
LMArena Text: coding14871485GLM 5.2
LMArena Text: software and IT14861473GLM 5.2
LMArena WebDev: React16171583GLM 5.2
LMArena WebDev: HTML15651556GLM 5.2
LMArena WebDev: from an imageNot measured1550Only Step 5 Preview 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 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
AA Intelligence Index33.743.7Step 5 Preview
Epoch Capabilities Index151.9Not measuredOnly GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index13
Agentic work benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
AA Agentic Index38.4Not measuredOnly GLM 5.2 measured
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 apps1184Not 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
LiveBench researchNot measured72.8%Only Step 5 Preview measured
LiveBench browserNot measured24.0%Only Step 5 Preview measured
LiveBench long agenticNot measured27.5%Only Step 5 Preview measured
SREBench (Vals AI)0.0%0.4%Step 5 Preview
Finance Agent (Vals AI)49.7%50.7%Step 5 Preview
Tax Agent Bench (Vals AI)Not measured27.3%Only Step 5 Preview measured
Legal Research Bench (Vals AI)31.3%40.4%Step 5 Preview
Excel Modeling Benchmark (Vals AI)61.5%57.1%GLM 5.2
BioMysteryBench (Vals AI)Not measured67.4%Only Step 5 Preview measured
Terminal-Bench-Science (Vals AI)Not measured2.9%Only Step 5 Preview measured
Vals Science IndexNot measured29.6%Only Step 5 Preview measured
τ²-Bench Telecom99.1%Not measuredOnly GLM 5.2 measured
APEX-Agents45.2%Not measuredOnly GLM 5.2 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 15 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
Reasoning benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
GPQA Diamond85.6%Not measuredOnly GLM 5.2 measured
LiveBench reasoningNot measured83.8%Only Step 5 Preview measured
MysteryMechanism (Vals AI)Not measured18.5%Only Step 5 Preview measured
GPQA Diamond (Artificial Analysis run)89.5%Not measuredOnly GLM 5.2 measured
Humanity's Last Exam (Artificial Analysis run)41.1%46.5%Step 5 Preview
GPQA Diamond (Epoch AI run)91.9%Not measuredOnly GLM 5.2 measured
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%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811461GLM 5.2
LMArena Text: expert14871485GLM 5.2
LMArena Text: hard prompts in English14811454GLM 5.2
LMArena Vision: diagramsNot measured1308Only Step 5 Preview measured
LMArena Vision: homeworkNot measured1265Only Step 5 Preview measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
OTIS Mock AIME86.4%Not measuredOnly GLM 5.2 measured
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%42.0%Step 5 Preview
LMArena Text: math14831458GLM 5.2
LMArena Text: mathematical work14891460GLM 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 Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
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
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891454GLM 5.2
LMArena Text: medicine and healthcare14751439GLM 5.2
LMArena Text: legal and government14861437GLM 5.2
LMArena Text: business and finance14511435GLM 5.2
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
Design Arena: websites1285Not measuredOnly GLM 5.2 measured
Design Arena: UI components1296Not measuredOnly GLM 5.2 measured
Design Arena: game dev1268Not measuredOnly GLM 5.2 measured
Design Arena: data viz1285Not measuredOnly GLM 5.2 measured
Design Arena: 3D1288Not measuredOnly GLM 5.2 measured
Design Arena: all code categories1288Not measuredOnly GLM 5.2 measured
Design Arena: SVG1214Not measuredOnly GLM 5.2 measured
Design Arena: ASCII art1190Not measuredOnly GLM 5.2 measured
LiveBench designNot measured76.9%Only Step 5 Preview measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index141
General benchmarks for GLM 5.2 and Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
LiveBench data analysisNot measured63.5%Only Step 5 Preview measured
Public Benefits Bench (Vals AI)Not measured58.1%Only Step 5 Preview measured
MedScribe (Vals AI)83.5%Not measuredOnly GLM 5.2 measured
IFBench73.3%Not measuredOnly GLM 5.2 measured
Long context reasoning (AA-LCR)78.3%88.3%Step 5 Preview
DTBench93.6%Not measuredOnly GLM 5.2 measured
LMCA45.8%Not measuredOnly GLM 5.2 measured
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
LMArena Text14701440GLM 5.2
LMArena Text: instruction following14661438GLM 5.2
LMArena Text: creative writing14631409GLM 5.2
LMArena VisionNot measured1265Only Step 5 Preview measured
LMArena Text: English14751447GLM 5.2
LMArena Text: non-English14591428GLM 5.2
LMArena Text: Chinese15221500GLM 5.2
LMArena Text: German1470Not measuredOnly GLM 5.2 measured
LMArena Text: Japanese14501383GLM 5.2
LMArena Text: Korean1440Not measuredOnly GLM 5.2 measured
LMArena Text: French14791438GLM 5.2
LMArena Text: Spanish14781441GLM 5.2
LMArena Text: Russian14681433GLM 5.2
LMArena Text: Polish1456Not measuredOnly GLM 5.2 measured
LMArena Text: multi-turn14691437GLM 5.2
LMArena Text: longer queries14801449GLM 5.2
LMArena Text: writing and language14631425GLM 5.2
LMArena Text: entertainment and media14501412GLM 5.2
LMArena Vision: EnglishNot measured1290Only Step 5 Preview measured
LMArena Vision: reading textNot measured1282Only Step 5 Preview measured
Neither model has a result on the other 18 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 Step 5 Preview
BenchmarkGLM 5.2Step 5 PreviewLeader
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$0.0035Fits in one request
Step 5 Preview$0.0024Fits in one request

Step 5 Preview costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.023Fits in one request
Step 5 Preview$0.058Fits in one request

GLM 5.2 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.077Fits in one request
Step 5 Preview$0.227Fits in one request

GLM 5.2 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 Step 5 Preview
SpecificationZ.ai GLM 5.2StepFun Step 5 Preview
MakerZ.aiStepFun
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026October 8, 2026
Context window1.05M1.05M
Input $/Mtok$0.0341$1.00
Output $/Mtok$7.00$2.70
Coding index57.771.4
Independent measurements behind it12
Cost per solved task$0.86Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Step 5 Preview for coding?

Pick Step 5 Preview for coding: it scores 71.4 on the Vibe Coding Index against 57.7 for GLM 5.2. Pick GLM 5.2 when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Which is cheaper, GLM 5.2 or Step 5 Preview?

GLM 5.2 costs $0.0341 per million input tokens and $7.00 per million output tokens; Step 5 Preview costs $1.00 and $2.70. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, GLM 5.2 or Step 5 Preview?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.2 vs Step 5 Preview comparison based on?

39 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 1 independent benchmark and Step 5 Preview's on 2. Where only one model has a result, the page says so instead of naming a winner.

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