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Claude Opus 5.5 vs GLM 5.3

Updated October 21, 2026. 83 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Claude Opus 5.5Anthropic
Coding
92.2
Overall
57.6
Output $/M
$20.00
80
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
3
vs boards won of 83 shared

Decision reading

Pick Claude Opus 5.5 for coding: it scores 92.2 on the Vibe Coding Index against 73.1 for GLM 5.3.

Pick GLM 5.3 when output tokens cost 14.3x less, or when the context window is larger, 1.05M against 1M tokens.

Shared results
83
Claude Opus 5.5 only
24
GLM 5.3 only
17
Like-for-like categories
8 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
Claude Opus 5.5 92.2 from 2 benchmarks, GLM 5.3 73.1 from 2 benchmarks.
Widest category
Math, 25.8 points apart on average over 6 shared benchmarks.
Widest single gap
LiveBench browser: Claude Opus 5.5 80.0% against GLM 5.3 0.0%, 80.0 points apart on a 0 to 100 scale.
Output price
Claude Opus 5.5 $20.00 against GLM 5.3 $1.40 per million output tokens, 14.3x apart.
Context window
Claude Opus 5.5 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 Opus 5.5BarsGLM 5.3Ties
Coding180
Intelligence20
Agentic work121
Reasoning81
Math60
Knowledge60
Web design80
General201
All shared boards803
CodingLike-for-likeFour of these boards feed the Vibe Coding Index180
Coding benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
LiveBench coding96.3%82.8%Claude Opus 5.5
LMArena WebDev18071621Claude Opus 5.5
AA Coding IndexNot measured74.8Only GLM 5.3 measured
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)65.2%38.9%Claude Opus 5.5
Vibe Code Bench (Vals AI)90.3%78.1%Claude Opus 5.5
Code migration (Vals AI)66.7%44.2%Claude Opus 5.5
ProgramBench (Vals AI)18.5%1.5%Claude Opus 5.5
IOI (Vals AI)95.1%68.4%Claude Opus 5.5
Terminal-Bench 2.1 (Vals AI)87.6%71.5%Claude Opus 5.5
SWE-bench Multimodal (Vals AI)70.2%Not measuredOnly Claude Opus 5.5 measured
Vibe Code Bench 1-100 (Vals AI)30.4%20.0%Claude Opus 5.5
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)74.6%72.1%Claude Opus 5.5
SciCode66.9%59.0%Claude Opus 5.5
FrontierCode54.6%40.1%Claude Opus 5.5
FrontierSWE62.3%30.2%Claude Opus 5.5
WeirdMLNot measured75.4%Only GLM 5.3 measured
MirrorCode77.4%Not measuredOnly Claude Opus 5.5 measured
ALE-Bench2147.41317.4Claude Opus 5.5
LMArena Text: coding15451497Claude Opus 5.5
LMArena Text: software and IT15381493Claude Opus 5.5
LMArena WebDev: React18551641Claude Opus 5.5
LMArena WebDev: HTML17531595Claude Opus 5.5
LMArena WebDev: from an image1753Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
AA Intelligence Index57.644.8Claude Opus 5.5
Epoch Capabilities Index168.8156.0Claude Opus 5.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index121
Agentic work benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building web apps1316Not measuredOnly Claude Opus 5.5 measured
Design Arena: agents building mobile apps13111194Claude Opus 5.5
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1255Only GLM 5.3 measured
Design Arena: spreadsheets1359Not measuredOnly Claude Opus 5.5 measured
LiveBench research85.0%73.2%Claude Opus 5.5
LiveBench browser80.0%0.0%Claude Opus 5.5
LiveBench long agentic85.0%35.9%Claude Opus 5.5
SREBench (Vals AI)33.6%9.2%Claude Opus 5.5
Finance Agent (Vals AI)58.6%55.8%Claude Opus 5.5
Tax Agent Bench (Vals AI)45.6%39.7%Claude Opus 5.5
Legal Research Bench (Vals AI)50.5%49.0%Claude Opus 5.5
Harvey's Legal Agent Benchmark (Vals AI)9.2%14.6%GLM 5.3
Excel Modeling Benchmark (Vals AI)75.9%56.3%Claude Opus 5.5
BioMysteryBench (Vals AI)79.3%Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench-Science (Vals AI)47.1%5.7%Claude Opus 5.5
Vals Science Index58.7%Not measuredOnly Claude Opus 5.5 measured
Time Horizon Index (Vals AI)91.3%Not measuredOnly Claude Opus 5.5 measured
CUA-bench (Vals AI)14.0%Not measuredOnly Claude Opus 5.5 measured
Robo-bench (Vals AI)14.6%Not measuredOnly Claude Opus 5.5 measured
APEX-Agents73.5%56.6%Claude Opus 5.5
Remote Labor Index21.3%Not measuredOnly Claude Opus 5.5 measured
SpireClimb3.3%Not measuredOnly Claude Opus 5.5 measured
Vending-Bench 2$9,235$8,164Claude Opus 5.5
LMArena Search1213Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 13 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
Reasoning benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoning92.2%85.8%Claude Opus 5.5
MysteryMechanism (Vals AI)49.5%23.0%Claude Opus 5.5
GPQA Diamond (Artificial Analysis run)Not measured91.7%Only GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)61.4%42.3%Claude Opus 5.5
GPQA Diamond (Epoch AI run)90.6%90.9%GLM 5.3
ARC-AGI98.5%Not measuredOnly Claude Opus 5.5 measured
ARC-AGI-293.3%Not measuredOnly Claude Opus 5.5 measured
Chess puzzles67.0%37.0%Claude Opus 5.5
Mystery game puzzles71.0%33.0%Claude Opus 5.5
EBR-bench71.4%Not measuredOnly Claude Opus 5.5 measured
LMArena Text: hard prompts15361490Claude Opus 5.5
LMArena Text: expert15491514Claude Opus 5.5
LMArena Text: hard prompts in English15351496Claude Opus 5.5
LMArena Vision: diagrams1345Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: homework1362Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index60
Math benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
OTIS Mock AIME100.0%91.1%Claude Opus 5.5
FrontierMath, tiers 1 to 391.2%68.8%Claude Opus 5.5
FrontierMath, tier 495.0%29.3%Claude Opus 5.5
ProofBench100.0%49.0%Claude Opus 5.5
LMArena Text: math15181495Claude Opus 5.5
LMArena Text: mathematical work15161497Claude Opus 5.5
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
Knowledge benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)Not measured84.8%Only GLM 5.3 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)49.8%42.9%Claude Opus 5.5
SimpleQA Verified72.2%41.0%Claude Opus 5.5
LMArena Text: science15241498Claude Opus 5.5
LMArena Text: medicine and healthcare15091481Claude Opus 5.5
LMArena Text: legal and government15181473Claude Opus 5.5
LMArena Text: business and finance14941459Claude Opus 5.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
Design Arena: websites13351295Claude Opus 5.5
Design Arena: UI components13901314Claude Opus 5.5
Design Arena: game dev14571311Claude Opus 5.5
Design Arena: data viz13971260Claude Opus 5.5
Design Arena: 3D14771332Claude Opus 5.5
Design Arena: all code categories13901304Claude Opus 5.5
Design Arena: SVG13321292Claude Opus 5.5
Design Arena: ASCII artNot measured1213Only GLM 5.3 measured
LiveBench design86.9%77.3%Claude Opus 5.5
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
General benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 5.3Leader
Vals Index70.2%59.7%Claude Opus 5.5
LiveBench data analysis80.3%70.2%Claude Opus 5.5
Public Benefits Bench (Vals AI)70.6%68.5%Claude Opus 5.5
SAGE (Vals AI)45.8%Not measuredOnly Claude Opus 5.5 measured
MedScribe (Vals AI)91.4%88.8%Claude Opus 5.5
Long context reasoning (AA-LCR)84.7%79.7%Claude Opus 5.5
DTBench98.9%87.7%Claude Opus 5.5
LMCA68.2%55.5%Claude Opus 5.5
Furniture assembly83.3%Not measuredOnly Claude Opus 5.5 measured
LMArena Text15161474Claude Opus 5.5
LMArena Text: instruction following15371479Claude Opus 5.5
LMArena Text: creative writing15351458Claude Opus 5.5
LMArena Vision1321Not measuredOnly Claude Opus 5.5 measured
LMArena Text: English15191482Claude Opus 5.5
LMArena Text: non-English15071459Claude Opus 5.5
LMArena Text: Chinese15821532Claude Opus 5.5
LMArena Text: GermanNot measured1488Only GLM 5.3 measured
LMArena Text: Japanese15341460Claude Opus 5.5
LMArena Text: KoreanNot measured1460Only GLM 5.3 measured
LMArena Text: French14931503GLM 5.3
LMArena Text: Spanish15081462Claude Opus 5.5
LMArena Text: Russian15171469Claude Opus 5.5
LMArena Text: PolishNot measured1466Only GLM 5.3 measured
LMArena Text: multi-turn15011472Claude Opus 5.5
LMArena Text: longer queries15321483Claude Opus 5.5
LMArena Text: writing and language15321469Claude Opus 5.5
LMArena Text: entertainment and media15151447Claude Opus 5.5
LMArena Vision: English1317Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: reading text1326Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: creative writing1320Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 5.5 and GLM 5.3
BenchmarkClaude Opus 5.5GLM 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 Opus 5.5$0.014Fits in one request
GLM 5.3$0.0008Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Claude Opus 5.5$0.260Fits in one request
GLM 5.3$0.0081Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 5.5$1.00Fits in one request
GLM 5.3$0.030Fits 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 Opus 5.5 and GLM 5.3
SpecificationAnthropic Claude Opus 5.5Z.ai GLM 5.3
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedSeptember 22, 2026August 18, 2026
Context window1M1.05M
Input $/Mtok$4.00$0.079
Output $/Mtok$20.00$1.40
Coding index92.273.1
Independent measurements behind it22
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 5.5 better than GLM 5.3 for coding?

Pick Claude Opus 5.5 for coding: it scores 92.2 on the Vibe Coding Index against 73.1 for GLM 5.3. Pick GLM 5.3 when output tokens cost 14.3x less, or when the context window is larger, 1.05M against 1M tokens.

Which is cheaper, Claude Opus 5.5 or GLM 5.3?

Claude Opus 5.5 costs $4.00 per million input tokens and $20.00 per million output tokens; GLM 5.3 costs $0.079 and $1.40. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, Claude Opus 5.5 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the Claude Opus 5.5 vs GLM 5.3 comparison based on?

83 benchmarks have a result for both models as of October 21, 2026. Claude Opus 5.5's coding index rests on 2 independent benchmarks and GLM 5.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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