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GLM 5 vs GLM 5.3

GLM 5.3 leads GLM 5 on coding, 70.6 to 41.7 on the Vibe Coding Index, and GLM 5 costs 1.4x less per output token ($3.20 vs $4.40 per million). Updated October 22, 2026; 58 benchmarks have a result for both.

Replace GLM 5 Replace GLM 5.3
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
1
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
57
vs boards won of 58 shared

GLM 5 or GLM 5.3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 41.7 for GLM 5.

Pick GLM 5 for output tokens 1.4x cheaper ($3.20 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI), where it leads.

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
58
GLM 5 only
18
GLM 5.3 only
42
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 41.7 from 3 benchmarks, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Coding, 21.7 points apart on average over 10 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5 23.4% against GLM 5.3 78.1%, 54.8 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against GLM 5.3 $4.40 per million output tokens, 1.4x apart.
Context window
GLM 5 takes 205K tokens, GLM 5.3 1.05M.

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 5BarsGLM 5.3Ties
Coding19
Intelligence02
Agentic work03
Reasoning07
Math03
Knowledge07
Web design08
General018
All shared boards157
CodingLike-for-likeSix of these boards feed the Vibe Coding Index19
Coding benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev14351621GLM 5.3
AA Coding IndexNot measured74.8Only GLM 5.3 measured
LiveCodeBench (Vals AI)81.9%80.5%GLM 5
SWE-bench Verified (Vals AI)71.4%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)23.4%78.1%GLM 5.3
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
SciCodeNot measured59.0%Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)43.2%Not measuredOnly GLM 5 measured
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdML48.2%75.4%GLM 5.3
ALE-Bench765.61317.4GLM 5.3
LMArena Text: coding14611497GLM 5.3
LMArena Text: software and IT14601493GLM 5.3
LMArena WebDev: React14241641GLM 5.3
LMArena WebDev: HTML14501595GLM 5.3
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
AA Intelligence Index27.944.8GLM 5.3
Epoch Capabilities Index145.8156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building full-stack apps1123Not measuredOnly GLM 5 measured
Design Arena: agents building mobile apps11361193GLM 5.3
Design Arena: agents building Android apps1147Not measuredOnly GLM 5 measured
Design Arena: agents building Godot games1144Not measuredOnly GLM 5 measured
Design Arena: HTML slides10981189GLM 5.3
Design Arena: PowerPoint slidesNot measured1231Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom98.2%Not measuredOnly GLM 5 measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2$4,432$8,164GLM 5.3
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
GPQA Diamond83.3%88.1%GLM 5.3
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)82.0%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)29.3%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)87.8%90.9%GLM 5.3
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%Not measuredOnly GLM 5 measured
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts14521490GLM 5.3
LMArena Text: expert14541514GLM 5.3
LMArena Text: hard prompts in English14601496GLM 5.3
Kagi LLM Benchmark75.0%Not measuredOnly GLM 5 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 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%91.1%GLM 5.3
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math14401495GLM 5.3
LMArena Text: mathematical work14511497GLM 5.3
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index07
Knowledge benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
MMLU Pro86.0%86.8%GLM 5.3
LegalBench (Vals AI)84.1%84.8%GLM 5.3
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%72.4%GLM 5.3
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science14601498GLM 5.3
LMArena Text: medicine and healthcare14551481GLM 5.3
LMArena Text: legal and government14531473GLM 5.3
LMArena Text: business and finance14301459GLM 5.3
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
Design Arena: websites12471294GLM 5.3
Design Arena: UI components12281311GLM 5.3
Design Arena: game dev12341311GLM 5.3
Design Arena: data viz12281261GLM 5.3
Design Arena: 3D12281331GLM 5.3
Design Arena: all code categories12461303GLM 5.3
Design Arena: SVG11671292GLM 5.3
Design Arena: ASCII art11471208GLM 5.3
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
IFBench72.3%Not measuredOnly GLM 5 measured
Long context reasoning (AA-LCR)75.7%79.7%GLM 5.3
DTBenchNot measured87.7%Only GLM 5.3 measured
LMCANot measured55.5%Only GLM 5.3 measured
CL-bench18.7%Not measuredOnly GLM 5 measured
LMArena Text14461474GLM 5.3
LMArena Text: instruction following14281479GLM 5.3
LMArena Text: creative writing14381458GLM 5.3
LMArena Text: English14581482GLM 5.3
LMArena Text: non-English14301459GLM 5.3
LMArena Text: Chinese15111532GLM 5.3
LMArena Text: German14471488GLM 5.3
LMArena Text: Japanese14161460GLM 5.3
LMArena Text: Korean14231460GLM 5.3
LMArena Text: French14561503GLM 5.3
LMArena Text: Spanish14541462GLM 5.3
LMArena Text: Russian14361469GLM 5.3
LMArena Text: Polish14341466GLM 5.3
LMArena Text: multi-turn14561472GLM 5.3
LMArena Text: longer queries14461483GLM 5.3
LMArena Text: writing and language14371469GLM 5.3
LMArena Text: entertainment and media14261447GLM 5.3
Neither model has a result on the other 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and GLM 5.3
BenchmarkGLM 5GLM 5.3Leader
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$0.0026Fits in one request
GLM 5.3$0.0036Fits in one request

GLM 5 costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits in one request
GLM 5.3$0.083Fits in one request

GLM 5 costs less

Agent loop

200K input + 10K output tokens

GLM 5$0.232Exceeds the context window
GLM 5.3$0.324Fits in one request

GLM 5 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 and GLM 5.3
SpecificationZ.ai GLM 5Z.ai GLM 5.3
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedFebruary 11, 2026August 18, 2026
Context window205K1.05M
Input $/Mtok$1.00$1.40
Output $/Mtok$3.20$4.40
Coding index41.770.6
Independent measurements behind it36
Cost per solved task$0.63$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 41.7 for GLM 5. Pick GLM 5 for output tokens 1.4x cheaper ($3.20 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 5 or GLM 5.3?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and GLM 5.3 $0.36.

Which has the bigger context window, GLM 5 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5 vs GLM 5.3 comparison based on?

58 benchmarks have a result for both models as of October 22, 2026. GLM 5's coding index rests on 3 independent benchmarks and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-vs-glm-5-3">LLMBenchmark.ai, GLM 5 vs GLM 5.3</a></p>