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GLM 5.3 vs Beam 1.0 501B A23B

Updated October 21, 2026. 28 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Replace Beam 1.0 501B A23B
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
28
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
0
vs boards won of 28 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.

Pick Beam 1.0 501B A23B when output tokens cost 1.4x less.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
28
GLM 5.3 only
72
Beam 1.0 501B A23B only
0
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 5.3 73.1 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Coding, 17.7 points apart on average over 5 shared benchmarks.
Widest single gap
LMArena WebDev: HTML: GLM 5.3 1595 against Beam 1.0 501B A23B 1436, 26.5 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $1.40 against Beam 1.0 501B A23B $1.00 per million output tokens, 1.4x 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 5.3BarsBeam 1.0 501B A23BTies
Coding50
Reasoning30
Math20
Knowledge40
Web design20
General120
All shared boards280
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
Coding benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev16211437GLM 5.3
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 4 (Vals AI)38.9%Not measuredOnly GLM 5.3 measured
Vibe Code Bench (Vals AI)78.1%Not measuredOnly GLM 5.3 measured
Code migration (Vals AI)44.2%Not measuredOnly GLM 5.3 measured
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%Not measuredOnly GLM 5.3 measured
SciCode59.0%Not measuredOnly GLM 5.3 measured
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971473GLM 5.3
LMArena Text: software and IT14931461GLM 5.3
LMArena WebDev: React16411443GLM 5.3
LMArena WebDev: HTML15951436GLM 5.3
Neither model has a result on the other 15 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
AA Intelligence Index44.8Not measuredOnly GLM 5.3 measured
Epoch Capabilities Index156.0Not measuredOnly GLM 5.3 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
Agentic work benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1255Not measuredOnly GLM 5.3 measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%Not measuredOnly GLM 5.3 measured
Tax Agent Bench (Vals AI)39.7%Not measuredOnly GLM 5.3 measured
Legal Research Bench (Vals AI)49.0%Not measuredOnly GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%Not measuredOnly GLM 5.3 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)91.7%Not measuredOnly GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)42.3%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Epoch AI run)90.9%Not measuredOnly GLM 5.3 measured
Chess puzzles37.0%Not measuredOnly GLM 5.3 measured
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901442GLM 5.3
LMArena Text: expert15141447GLM 5.3
LMArena Text: hard prompts in English14961451GLM 5.3
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
OTIS Mock AIME91.1%Not measuredOnly GLM 5.3 measured
FrontierMath, tiers 1 to 368.8%Not measuredOnly GLM 5.3 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%Not measuredOnly GLM 5.3 measured
LMArena Text: math14951459GLM 5.3
LMArena Text: mathematical work14971475GLM 5.3
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 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
MMLU Pro86.8%Not measuredOnly GLM 5.3 measured
LegalBench (Vals AI)84.8%Not measuredOnly GLM 5.3 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 5.3 measured
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
LMArena Text: science14981430GLM 5.3
LMArena Text: medicine and healthcare14811376GLM 5.3
LMArena Text: legal and government14731433GLM 5.3
LMArena Text: business and finance14591430GLM 5.3
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index20
Web design benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
Design Arena: websites12951253GLM 5.3
Design Arena: UI components1314Not measuredOnly GLM 5.3 measured
Design Arena: game dev13111222GLM 5.3
Design Arena: data viz1260Not measuredOnly GLM 5.3 measured
Design Arena: 3D1332Not measuredOnly GLM 5.3 measured
Design Arena: all code categories1304Not measuredOnly GLM 5.3 measured
Design Arena: SVG1292Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1213Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
General benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
MedScribe (Vals AI)88.8%Not measuredOnly GLM 5.3 measured
Long context reasoning (AA-LCR)79.7%Not measuredOnly GLM 5.3 measured
DTBench87.7%Not measuredOnly GLM 5.3 measured
LMCA55.5%Not measuredOnly GLM 5.3 measured
LMArena Text14741425GLM 5.3
LMArena Text: instruction following14791430GLM 5.3
LMArena Text: creative writing14581390GLM 5.3
LMArena Text: English14821436GLM 5.3
LMArena Text: non-English14591413GLM 5.3
LMArena Text: Chinese15321424GLM 5.3
LMArena Text: German1488Not measuredOnly GLM 5.3 measured
LMArena Text: Japanese1460Not measuredOnly GLM 5.3 measured
LMArena Text: Korean1460Not measuredOnly GLM 5.3 measured
LMArena Text: French15031461GLM 5.3
LMArena Text: Spanish1462Not measuredOnly GLM 5.3 measured
LMArena Text: Russian14691412GLM 5.3
LMArena Text: Polish1466Not measuredOnly GLM 5.3 measured
LMArena Text: multi-turn14721414GLM 5.3
LMArena Text: longer queries14831429GLM 5.3
LMArena Text: writing and language14691400GLM 5.3
LMArena Text: entertainment and media14471399GLM 5.3
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 and Beam 1.0 501B A23B
BenchmarkGLM 5.3Beam 1.0 501B A23BLeader
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 5.3$0.0008Fits in one request
Beam 1.0 501B A23B$0.0008Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request
Beam 1.0 501B A23B$0.070Fits in one request

GLM 5.3 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 5.3 and Beam 1.0 501B A23B
SpecificationZ.ai GLM 5.3Reflection Beam 1.0 501B A23B
MakerZ.aiReflection
WeightsOpen weightsOpen weights
ReleasedAugust 18, 2026October 16, 2026
Context window1.05M1.05M
Input $/Mtok$0.079$0.30
Output $/Mtok$1.40$1.00
Coding index73.133.8
Independent measurements behind it21
Cost per solved task$0.36Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Beam 1.0 501B A23B for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when output tokens cost 1.4x less.

Which is cheaper, GLM 5.3 or Beam 1.0 501B A23B?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, GLM 5.3 or Beam 1.0 501B A23B?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.3 vs Beam 1.0 501B A23B comparison based on?

28 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Beam 1.0 501B A23B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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