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Claude Sonnet 4.5 vs GLM 5.3

GLM 5.3 leads Claude Sonnet 4.5 on coding, 70.6 to 39.5 on the Vibe Coding Index, and GLM 5.3 costs 3.4x less per output token ($4.40 vs $15.00 per million). Updated October 22, 2026; 65 benchmarks have a result for both.

Replace Claude Sonnet 4.5 Replace GLM 5.3
Claude Sonnet 4.5Anthropic
Coding
39.5
Overall
49.5
Output $/M
$15.00
3
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
62
vs boards won of 65 shared

Claude Sonnet 4.5 or GLM 5.3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 39.5 for Claude Sonnet 4.5.

Pick Claude Sonnet 4.5 for work measured by MMLU Pro or TaxEval (Vals AI), where it leads.

Model AClaude Sonnet 4.5Anthropic39.5/1003 independent benchmarks$1.16 per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
65
Claude Sonnet 4.5 only
30
GLM 5.3 only
35
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
Claude Sonnet 4.5 39.5 from 3 benchmarks, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Coding, 24.5 points apart on average over 10 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): Claude Sonnet 4.5 22.6% against GLM 5.3 78.1%, 55.5 points apart on a 0 to 100 scale.
Output price
Claude Sonnet 4.5 $15.00 against GLM 5.3 $4.40 per million output tokens, 3.4x apart.
Context window
Claude Sonnet 4.5 takes 1M 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
CategoryClaude Sonnet 4.5BarsGLM 5.3Ties
Coding010
Intelligence02
Agentic work03
Reasoning07
Math06
Knowledge36
Web design08
General020
All shared boards362
CodingLike-for-likeSix of these boards feed the Vibe Coding Index010
Coding benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
SWE-bench Verified74.8%Not measuredOnly Claude Sonnet 4.5 measured
SWE-bench Multilingual67.0%Not measuredOnly Claude Sonnet 4.5 measured
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev13941621GLM 5.3
AA Coding Index52.174.8GLM 5.3
LiveCodeBench (Vals AI)73.0%80.5%GLM 5.3
SWE-bench Verified (Vals AI)70.0%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)22.6%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-Bench46.5%Not measuredOnly Claude Sonnet 4.5 measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
GSO-Bench14.7%Not measuredOnly Claude Sonnet 4.5 measured
WeirdML47.7%75.4%GLM 5.3
Cybench60.0%Not measuredOnly Claude Sonnet 4.5 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding14881497GLM 5.3
LMArena Text: software and IT14731493GLM 5.3
LMArena WebDev: React13861641GLM 5.3
LMArena WebDev: HTML13991595GLM 5.3
Neither model has a result on the other 11 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
AA Intelligence Index20.744.8GLM 5.3
Epoch Capabilities Index146.7156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
AA Agentic Index15.853.1GLM 5.3
Design Arena: agents building web apps1060Not measuredOnly Claude Sonnet 4.5 measured
Design Arena: agents building full-stack apps1050Not measuredOnly Claude Sonnet 4.5 measured
Design Arena: agents building mobile apps11371194GLM 5.3
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1230Only 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
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
METR time horizons67.4%Not measuredOnly Claude Sonnet 4.5 measured
Remote Labor Index2.1%Not measuredOnly Claude Sonnet 4.5 measured
GDPval42.5%Not measuredOnly Claude Sonnet 4.5 measured
DeepResearch Bench52.6%Not measuredOnly Claude Sonnet 4.5 measured
OSWorld62.9%Not measuredOnly Claude Sonnet 4.5 measured
Vending-Bench 2$3,839$8,164GLM 5.3
Berkeley Function Calling Leaderboard (BFCL)73.2%Not measuredOnly Claude Sonnet 4.5 measured
Neither model has a result on the other 17 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
GPQA Diamond81.6%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
MMMU Pro (Vals AI)79.3%Not measuredOnly Claude Sonnet 4.5 measured
GPQA Diamond (Artificial Analysis run)Not measured91.7%Only GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)Not measured42.3%Only GLM 5.3 measured
GPQA Diamond (Epoch AI run)82.3%90.9%GLM 5.3
ARC-AGI63.7%Not measuredOnly Claude Sonnet 4.5 measured
ARC-AGI-213.6%Not measuredOnly Claude Sonnet 4.5 measured
SimpleBench54.3%Not measuredOnly Claude Sonnet 4.5 measured
Chess puzzles10.0%37.0%GLM 5.3
Mystery game puzzles17.0%33.0%GLM 5.3
EBR-bench2.4%Not measuredOnly Claude Sonnet 4.5 measured
VPCT39.8%Not measuredOnly Claude Sonnet 4.5 measured
LMArena Text: hard prompts14621490GLM 5.3
LMArena Text: expert14831514GLM 5.3
LMArena Text: hard prompts in English14711496GLM 5.3
Kagi LLM Benchmark57.9%Not measuredOnly Claude Sonnet 4.5 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index06
Math benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
AIME88.2%Not measuredOnly Claude Sonnet 4.5 measured
MGSM (Vals AI)94.3%Not measuredOnly Claude Sonnet 4.5 measured
OTIS Mock AIME77.8%91.1%GLM 5.3
FrontierMath, tiers 1 to 323.9%68.8%GLM 5.3
FrontierMath, tier 42.4%29.3%GLM 5.3
ProofBench19.0%49.0%GLM 5.3
MATH level 597.7%Not measuredOnly Claude Sonnet 4.5 measured
LMArena Text: math14491495GLM 5.3
LMArena Text: mathematical work14621497GLM 5.3
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index36
Knowledge benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
MMLU Pro87.4%86.8%Claude Sonnet 4.5
LegalBench (Vals AI)84.1%84.8%GLM 5.3
CaseLaw (Vals AI)62.2%Not measuredOnly Claude Sonnet 4.5 measured
CorpFin (Vals AI)62.0%Not measuredOnly Claude Sonnet 4.5 measured
TaxEval (Vals AI)73.3%72.4%Claude Sonnet 4.5
MortgageTax (Vals AI)64.0%Not measuredOnly Claude Sonnet 4.5 measured
MedQA (Vals AI)94.7%Not measuredOnly Claude Sonnet 4.5 measured
MedCode (Vals AI)44.1%42.9%Claude Sonnet 4.5
SimpleQA Verified30.7%41.0%GLM 5.3
LMArena Text: science14481498GLM 5.3
LMArena Text: medicine and healthcare14441481GLM 5.3
LMArena Text: legal and government14511473GLM 5.3
LMArena Text: business and finance14401459GLM 5.3
FACTS Benchmark Suite40.6%Not measuredOnly Claude Sonnet 4.5 measured
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 5.3Leader
Design Arena: websites11891295GLM 5.3
Design Arena: UI components11701314GLM 5.3
Design Arena: game dev11661311GLM 5.3
Design Arena: data viz11671260GLM 5.3
Design Arena: 3D11571332GLM 5.3
Design Arena: all code categories11821304GLM 5.3
Design Arena: SVG11151294GLM 5.3
Design Arena: ASCII art12051208GLM 5.3
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 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
SAGE (Vals AI)36.1%Not measuredOnly Claude Sonnet 4.5 measured
MedScribe (Vals AI)84.5%88.8%GLM 5.3
Long context reasoning (AA-LCR)Not measured79.7%Only GLM 5.3 measured
DTBench83.2%87.7%GLM 5.3
LMCA38.8%55.5%GLM 5.3
LMArena Text14381474GLM 5.3
LMArena Text: instruction following14591479GLM 5.3
LMArena Text: creative writing14421458GLM 5.3
LMArena Text: English14501482GLM 5.3
LMArena Text: non-English14251459GLM 5.3
LMArena Text: Chinese14601532GLM 5.3
LMArena Text: German14271488GLM 5.3
LMArena Text: Japanese13901460GLM 5.3
LMArena Text: Korean14021460GLM 5.3
LMArena Text: French14591503GLM 5.3
LMArena Text: Spanish14581462GLM 5.3
LMArena Text: Russian14371469GLM 5.3
LMArena Text: Polish14231466GLM 5.3
LMArena Text: multi-turn14651472GLM 5.3
LMArena Text: longer queries14751483GLM 5.3
LMArena Text: writing and language14421469GLM 5.3
LMArena Text: entertainment and media14281447GLM 5.3
LMArena Document1450Not measuredOnly Claude Sonnet 4.5 measured
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 Claude Sonnet 4.5 and GLM 5.3
BenchmarkClaude Sonnet 4.5GLM 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

Claude Sonnet 4.5$0.011Fits in one request
GLM 5.3$0.0036Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Claude Sonnet 4.5$0.195Fits in one request
GLM 5.3$0.083Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Claude Sonnet 4.5$0.750Fits in one request
GLM 5.3$0.324Fits 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 Claude Sonnet 4.5 and GLM 5.3
SpecificationAnthropic Claude Sonnet 4.5Z.ai GLM 5.3
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedSeptember 29, 2025August 18, 2026
Context window1M1.05M
Input $/Mtok$3.00$1.40
Output $/Mtok$15.00$4.40
Coding index39.570.6
Independent measurements behind it36
Cost per solved task$1.16$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Sonnet 4.5 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 39.5 for Claude Sonnet 4.5. Pick Claude Sonnet 4.5 for work measured by MMLU Pro or TaxEval (Vals AI), where it leads.

Which is cheaper, Claude Sonnet 4.5 or GLM 5.3?

Claude Sonnet 4.5 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Per solved SWE-bench Verified task, Claude Sonnet 4.5 costs $1.16 and GLM 5.3 $0.36.

Which has the bigger context window, Claude Sonnet 4.5 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the Claude Sonnet 4.5 vs GLM 5.3 comparison based on?

65 benchmarks have a result for both models as of October 22, 2026. Claude Sonnet 4.5'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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