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Claude Opus 4.8 vs GLM 4.5

Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Claude Opus 4.8Anthropic
Coding
66.0
Overall
41.8
Output $/M
$25.00
49
GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
0
vs boards won of 49 shared

Decision reading

Claude Opus 4.8 and GLM 4.5 are level for coding, 66.0 against 64.2 on the Vibe Coding Index, so price and context decide.

Pick GLM 4.5 when output tokens cost 11.4x less. Pick Claude Opus 4.8 when the context window is larger, 1M against 131K tokens, or when what matters is LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Shared results
49
Claude Opus 4.8 only
77
GLM 4.5 only
11
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 4.8 66.0 from 2 benchmarks, GLM 4.5 64.2 from 1 benchmark.
Widest category
Agentic work, 51.5 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Claude Opus 4.8 94.4% against GLM 4.5 43.0%, 51.5 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.8 $25.00 against GLM 4.5 $2.20 per million output tokens, 11.4x apart.
Context window
Claude Opus 4.8 takes 1M tokens, GLM 4.5 131K.

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 4.8BarsGLM 4.5Ties
Coding50
Intelligence10
Agentic work10
Reasoning60
Math20
Knowledge80
Web design70
General190
All shared boards490
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
Coding benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
SWE-bench VerifiedNot measured64.2%Only GLM 4.5 measured
LiveBench coding77.3%Not measuredOnly Claude Opus 4.8 measured
LMArena WebDev1556Not measuredOnly Claude Opus 4.8 measured
AA Coding Index74.3Not measuredOnly Claude Opus 4.8 measured
LiveCodeBench (Vals AI)87.8%67.5%Claude Opus 4.8
SWE-bench Verified (Vals AI)88.6%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench 4 (Vals AI)23.2%Not measuredOnly Claude Opus 4.8 measured
Vibe Code Bench (Vals AI)82.7%Not measuredOnly Claude Opus 4.8 measured
Code migration (Vals AI)47.3%Not measuredOnly Claude Opus 4.8 measured
ProgramBench (Vals AI)1.0%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench 2.1 (Vals AI)71.9%Not measuredOnly Claude Opus 4.8 measured
SkillsBench (Vals AI)59.2%Not measuredOnly Claude Opus 4.8 measured
LiveCodeBench (Artificial Analysis run)Not measured73.8%Only GLM 4.5 measured
SciCode54.4%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench Hard (Artificial Analysis run)58.3%22.0%Claude Opus 4.8
FrontierCode46.5%Not measuredOnly Claude Opus 4.8 measured
GSO-Bench47.1%Not measuredOnly Claude Opus 4.8 measured
WeirdML82.9%40.6%Claude Opus 4.8
ALE-BenchNot measured344.8Only GLM 4.5 measured
LMArena Text: coding14901434Claude Opus 4.8
LMArena Text: software and IT14881440Claude Opus 4.8
LMArena WebDev: React1557Not measuredOnly Claude Opus 4.8 measured
LMArena WebDev: HTML1543Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
AA Intelligence Index41.812.8Claude Opus 4.8
Epoch Capabilities Index159.0Not measuredOnly Claude Opus 4.8 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
AA Agentic Index41.9Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building web apps1233Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building full-stack apps1249Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building mobile apps1222Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building Android apps1237Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building Godot games1231Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building games1238Not measuredOnly Claude Opus 4.8 measured
Design Arena: HTML slides1221Not measuredOnly Claude Opus 4.8 measured
Design Arena: PowerPoint slides1227Not measuredOnly Claude Opus 4.8 measured
LiveBench research81.8%Not measuredOnly Claude Opus 4.8 measured
LiveBench browser96.0%Not measuredOnly Claude Opus 4.8 measured
LiveBench long agentic48.3%Not measuredOnly Claude Opus 4.8 measured
Finance Agent (Vals AI)53.9%Not measuredOnly Claude Opus 4.8 measured
Tax Agent Bench (Vals AI)30.0%Not measuredOnly Claude Opus 4.8 measured
Legal Research Bench (Vals AI)43.8%Not measuredOnly Claude Opus 4.8 measured
Excel Modeling Benchmark (Vals AI)69.4%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench-Science (Vals AI)4.3%Not measuredOnly Claude Opus 4.8 measured
Time Horizon Index (Vals AI)11.8%Not measuredOnly Claude Opus 4.8 measured
τ²-Bench Telecom94.4%43.0%Claude Opus 4.8
APEX-Agents48.9%Not measuredOnly Claude Opus 4.8 measured
Remote Labor Index8.3%Not measuredOnly Claude Opus 4.8 measured
OSWorld 2.020.6%Not measuredOnly Claude Opus 4.8 measured
DeepResearch Bench50.2%Not measuredOnly Claude Opus 4.8 measured
PostTrainBench33.8%Not measuredOnly Claude Opus 4.8 measured
Vending-Bench 2$5,787Not measuredOnly Claude Opus 4.8 measured
LMArena Search1204Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 13 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
GPQA Diamond92.4%72.2%Claude Opus 4.8
LiveBench reasoning89.2%Not measuredOnly Claude Opus 4.8 measured
MMMU Pro (Vals AI)86.6%Not measuredOnly Claude Opus 4.8 measured
GPQA Diamond (Artificial Analysis run)92.0%78.2%Claude Opus 4.8
Humanity's Last Exam (Artificial Analysis run)48.7%13.0%Claude Opus 4.8
GPQA Diamond (Epoch AI run)91.0%Not measuredOnly Claude Opus 4.8 measured
ARC-AGI92.5%Not measuredOnly Claude Opus 4.8 measured
ARC-AGI-272.1%Not measuredOnly Claude Opus 4.8 measured
SimpleBench64.8%Not measuredOnly Claude Opus 4.8 measured
Chess puzzles52.0%Not measuredOnly Claude Opus 4.8 measured
Mystery game puzzles36.0%Not measuredOnly Claude Opus 4.8 measured
EBR-bench28.6%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: hard prompts14821429Claude Opus 4.8
LMArena Text: expert15031433Claude Opus 4.8
LMArena Text: hard prompts in English14871429Claude Opus 4.8
LMArena Vision: diagrams1315Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: homework1337Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
AIMENot measured86.7%Only GLM 4.5 measured
MATH 500 (Vals AI)Not measured94.0%Only GLM 4.5 measured
MGSM (Vals AI)Not measured90.8%Only GLM 4.5 measured
AA Math IndexNot measured73.7Only GLM 4.5 measured
AIME 2025 (Artificial Analysis run)Not measured73.7%Only GLM 4.5 measured
OTIS Mock AIME98.3%Not measuredOnly Claude Opus 4.8 measured
FrontierMath, tiers 1 to 380.0%Not measuredOnly Claude Opus 4.8 measured
FrontierMath, tier 456.1%Not measuredOnly Claude Opus 4.8 measured
ProofBench69.0%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: math14861427Claude Opus 4.8
LMArena Text: mathematical work14971430Claude Opus 4.8
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
MMLU Pro89.6%81.2%Claude Opus 4.8
LegalBench (Vals AI)83.6%75.6%Claude Opus 4.8
CorpFin (Vals AI)66.7%61.0%Claude Opus 4.8
TaxEval (Vals AI)75.6%72.4%Claude Opus 4.8
MortgageTax (Vals AI)69.9%Not measuredOnly Claude Opus 4.8 measured
MedQA (Vals AI)Not measured90.0%Only GLM 4.5 measured
MedCode (Vals AI)53.2%Not measuredOnly Claude Opus 4.8 measured
MMLU Pro (Artificial Analysis run)Not measured83.5%Only GLM 4.5 measured
SimpleQA Verified53.0%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: science14771444Claude Opus 4.8
LMArena Text: medicine and healthcare14641455Claude Opus 4.8
LMArena Text: legal and government14771438Claude Opus 4.8
LMArena Text: business and finance14581420Claude Opus 4.8
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
Design Arena: websites12581170Claude Opus 4.8
Design Arena: UI components12561144Claude Opus 4.8
Design Arena: game dev12641145Claude Opus 4.8
Design Arena: data viz12401159Claude Opus 4.8
Design Arena: 3D12231170Claude Opus 4.8
Design Arena: all code categories12551165Claude Opus 4.8
Design Arena: SVG11941096Claude Opus 4.8
Design Arena: ASCII art1276Not measuredOnly Claude Opus 4.8 measured
LiveBench design78.8%Not measuredOnly Claude Opus 4.8 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
LiveBench data analysis66.0%Not measuredOnly Claude Opus 4.8 measured
Public Benefits Bench (Vals AI)68.1%Not measuredOnly Claude Opus 4.8 measured
SAGE (Vals AI)54.8%Not measuredOnly Claude Opus 4.8 measured
MedScribe (Vals AI)85.8%Not measuredOnly Claude Opus 4.8 measured
Vals Multimodal Index70.9%Not measuredOnly Claude Opus 4.8 measured
IFBench62.2%44.1%Claude Opus 4.8
Long context reasoning (AA-LCR)77.7%52.7%Claude Opus 4.8
DTBench94.9%Not measuredOnly Claude Opus 4.8 measured
LMCA57.5%Not measuredOnly Claude Opus 4.8 measured
Creative writing (Lech Mazur)Not measured73.4%Only GLM 4.5 measured
Surface Evolver Bench87.5%Not measuredOnly Claude Opus 4.8 measured
Furniture assembly42.5%Not measuredOnly Claude Opus 4.8 measured
LMArena Text14611430Claude Opus 4.8
LMArena Text: instruction following14761404Claude Opus 4.8
LMArena Text: creative writing14541395Claude Opus 4.8
LMArena Vision1294Not measuredOnly Claude Opus 4.8 measured
LMArena Text: English14681437Claude Opus 4.8
LMArena Text: non-English14501417Claude Opus 4.8
LMArena Text: Chinese15061465Claude Opus 4.8
LMArena Text: German14721407Claude Opus 4.8
LMArena Text: Japanese14421415Claude Opus 4.8
LMArena Text: Korean14311380Claude Opus 4.8
LMArena Text: French14811418Claude Opus 4.8
LMArena Text: Spanish14651454Claude Opus 4.8
LMArena Text: Russian14741413Claude Opus 4.8
LMArena Text: Polish14591422Claude Opus 4.8
LMArena Text: multi-turn14761415Claude Opus 4.8
LMArena Text: longer queries14831412Claude Opus 4.8
LMArena Text: writing and language14611403Claude Opus 4.8
LMArena Text: entertainment and media14401404Claude Opus 4.8
LMArena Vision: English1293Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: Chinese1346Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: reading text1303Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: humor1282Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: creative writing1301Not measuredOnly Claude Opus 4.8 measured
LMArena Document1475Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 10 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 4.8 and GLM 4.5
BenchmarkClaude Opus 4.8GLM 4.5Leader
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 4.8$0.018Fits in one request
GLM 4.5$0.0017Fits in one request

GLM 4.5 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.8$0.325Fits in one request
GLM 4.5$0.037Fits in one request

GLM 4.5 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.8$1.25Fits in one request
GLM 4.5$0.142Exceeds the context window

GLM 4.5 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 Claude Opus 4.8 and GLM 4.5
SpecificationAnthropic Claude Opus 4.8Z.ai GLM 4.5
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedMay 27, 2026July 25, 2025
Context window1M131K
Input $/Mtok$5.00$0.60
Output $/Mtok$25.00$2.20
Coding index66.064.2
Independent measurements behind it21
Cost per solved task$2.17Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.8 better than GLM 4.5 for coding?

Claude Opus 4.8 and GLM 4.5 are level for coding, 66.0 against 64.2 on the Vibe Coding Index, so price and context decide. Pick GLM 4.5 when output tokens cost 11.4x less. Pick Claude Opus 4.8 when the context window is larger, 1M against 131K tokens, or when what matters is LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, Claude Opus 4.8 or GLM 4.5?

Claude Opus 4.8 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 4.5 costs $0.60 and $2.20. Cost per solved task is known for Claude Opus 4.8 only.

Which has the bigger context window, Claude Opus 4.8 or GLM 4.5?

Claude Opus 4.8, with 1M tokens against 131K.

How many benchmarks is the Claude Opus 4.8 vs GLM 4.5 comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.8's coding index rests on 2 independent benchmarks and GLM 4.5's on 1. Where only one model has a result, the page says so instead of naming a winner.

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