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Claude Opus 4.6 vs GLM 5.3

GLM 5.3 leads Claude Opus 4.6 on coding, 70.6 to 57.2 on the Vibe Coding Index, and GLM 5.3 costs 5.7x less per output token ($4.40 vs $25.00 per million). Updated October 22, 2026; 68 benchmarks have a result for both.

Replace Claude Opus 4.6 Replace GLM 5.3
Claude Opus 4.6Anthropic
Coding
57.2
Overall
66.0
Output $/M
$25.00
43
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
25
vs boards won of 68 shared

Claude Opus 4.6 or GLM 5.3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 57.2 for Claude Opus 4.6.

Pick Claude Opus 4.6 for work measured by LiveCodeBench (Vals AI) or WeirdML, where it leads.

Model AClaude Opus 4.6Anthropic57.2/1004 independent benchmarks$1.56 per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
68
Claude Opus 4.6 only
42
GLM 5.3 only
32
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 Opus 4.6 57.2 from 4 benchmarks, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Coding, 10.4 points apart on average over 10 shared benchmarks.
Widest single gap
FrontierCode: Claude Opus 4.6 26.6% against GLM 5.3 40.1%, 20.8 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.6 $25.00 against GLM 5.3 $4.40 per million output tokens, 5.7x apart.
Context window
Claude Opus 4.6 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 4.6BarsGLM 5.3Ties
Coding46
Intelligence02
Agentic work12
Reasoning45
Math42
Knowledge90
Web design26
General192
All shared boards4325
CodingLike-for-likeSix of these boards feed the Vibe Coding Index46
Coding benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
SWE-bench Verified75.6%Not measuredOnly Claude Opus 4.6 measured
SWE-bench Multilingual72.0%Not measuredOnly Claude Opus 4.6 measured
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev15471621GLM 5.3
AA Coding IndexNot measured74.8Only GLM 5.3 measured
LiveCodeBench (Vals AI)84.7%80.5%Claude Opus 4.6
SWE-bench Verified (Vals AI)78.2%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)57.6%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-Bench Hard (Artificial Analysis run)48.5%Not measuredOnly Claude Opus 4.6 measured
Terminal-Bench79.8%Not measuredOnly Claude Opus 4.6 measured
FrontierCode26.6%40.1%GLM 5.3
FrontierSWENot measured30.2%Only GLM 5.3 measured
GSO-Bench41.2%Not measuredOnly Claude Opus 4.6 measured
WeirdML78.0%75.4%Claude Opus 4.6
Cybench93.0%Not measuredOnly Claude Opus 4.6 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding15341497Claude Opus 4.6
LMArena Text: software and IT15271493Claude Opus 4.6
LMArena WebDev: React15391641GLM 5.3
LMArena WebDev: HTML15591595GLM 5.3
LMArena WebDev: from an image1543Not measuredOnly Claude Opus 4.6 measured
Neither model has a result on the other 9 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
AA Intelligence Index31.944.8GLM 5.3
Epoch Capabilities Index155.7156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index12
Agentic work benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building web apps1206Not measuredOnly Claude Opus 4.6 measured
Design Arena: agents building full-stack apps1220Not measuredOnly Claude Opus 4.6 measured
Design Arena: agents building mobile apps12071194Claude Opus 4.6
Design Arena: agents building Android apps1197Not measuredOnly Claude Opus 4.6 measured
Design Arena: agents building games1212Not measuredOnly Claude Opus 4.6 measured
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
τ²-Bench Telecom92.1%Not measuredOnly Claude Opus 4.6 measured
APEX-Agents46.3%56.6%GLM 5.3
METR time horizons78.9%Not measuredOnly Claude Opus 4.6 measured
Remote Labor Index4.2%Not measuredOnly Claude Opus 4.6 measured
DeepResearch Bench55.3%Not measuredOnly Claude Opus 4.6 measured
Vending-Bench 2$8,018$8,164GLM 5.3
Neither model has a result on the other 17 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index45
Reasoning benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
GPQA Diamond89.7%88.1%Claude Opus 4.6
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
MMMU Pro (Vals AI)83.9%Not measuredOnly Claude Opus 4.6 measured
GPQA Diamond (Artificial Analysis run)89.6%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)39.9%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)90.5%90.9%GLM 5.3
ARC-AGI94.0%Not measuredOnly Claude Opus 4.6 measured
ARC-AGI-269.2%Not measuredOnly Claude Opus 4.6 measured
SimpleBench67.6%Not measuredOnly Claude Opus 4.6 measured
Chess puzzles21.0%37.0%GLM 5.3
Mystery game puzzles25.0%33.0%GLM 5.3
EBR-bench12.7%Not measuredOnly Claude Opus 4.6 measured
LMArena Text: hard prompts15251490Claude Opus 4.6
LMArena Text: expert15441514Claude Opus 4.6
LMArena Text: hard prompts in English15301496Claude Opus 4.6
LMArena Vision: diagrams1340Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: homework1337Not measuredOnly Claude Opus 4.6 measured
Kagi LLM Benchmark83.6%Not measuredOnly Claude Opus 4.6 measured
Neither model has a result on the other 3 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index42
Math benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
AIME95.6%Not measuredOnly Claude Opus 4.6 measured
OTIS Mock AIME94.4%91.1%Claude Opus 4.6
FrontierMath, tiers 1 to 366.0%68.8%GLM 5.3
FrontierMath, tier 426.8%29.3%GLM 5.3
ProofBench50.0%49.0%Claude Opus 4.6
LMArena Text: math15181495Claude Opus 4.6
LMArena Text: mathematical work15251497Claude Opus 4.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
MMLU Pro89.1%86.8%Claude Opus 4.6
LegalBench (Vals AI)85.3%84.8%Claude Opus 4.6
CaseLaw (Vals AI)62.1%Not measuredOnly Claude Opus 4.6 measured
CorpFin (Vals AI)67.0%Not measuredOnly Claude Opus 4.6 measured
TaxEval (Vals AI)76.0%72.4%Claude Opus 4.6
MortgageTax (Vals AI)68.5%Not measuredOnly Claude Opus 4.6 measured
MedQA (Vals AI)95.4%Not measuredOnly Claude Opus 4.6 measured
MedCode (Vals AI)49.1%42.9%Claude Opus 4.6
SimpleQA Verified47.0%41.0%Claude Opus 4.6
LMArena Text: science15191498Claude Opus 4.6
LMArena Text: medicine and healthcare15041481Claude Opus 4.6
LMArena Text: legal and government15121473Claude Opus 4.6
LMArena Text: business and finance14971459Claude Opus 4.6
FACTS Benchmark Suite41.5%Not measuredOnly Claude Opus 4.6 measured
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 5.3Leader
Design Arena: websites12911295GLM 5.3
Design Arena: UI components12811314GLM 5.3
Design Arena: game dev12771311GLM 5.3
Design Arena: data viz12791260Claude Opus 4.6
Design Arena: 3D12731332GLM 5.3
Design Arena: all code categories12881304GLM 5.3
Design Arena: SVG12271294GLM 5.3
Design Arena: ASCII art12551208Claude Opus 4.6
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index192
General benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 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)51.6%Not measuredOnly Claude Opus 4.6 measured
MedScribe (Vals AI)86.7%88.8%GLM 5.3
IFBench53.1%Not measuredOnly Claude Opus 4.6 measured
Long context reasoning (AA-LCR)78.0%79.7%GLM 5.3
DTBench91.2%87.7%Claude Opus 4.6
LMCA55.8%55.5%Claude Opus 4.6
CL-bench20.7%Not measuredOnly Claude Opus 4.6 measured
Furniture assembly28.3%Not measuredOnly Claude Opus 4.6 measured
CL-bench Life17.0%Not measuredOnly Claude Opus 4.6 measured
LMArena Text15011474Claude Opus 4.6
LMArena Text: instruction following15211479Claude Opus 4.6
LMArena Text: creative writing15051458Claude Opus 4.6
LMArena Vision1316Not measuredOnly Claude Opus 4.6 measured
LMArena Text: English15111482Claude Opus 4.6
LMArena Text: non-English14881459Claude Opus 4.6
LMArena Text: Chinese15511532Claude Opus 4.6
LMArena Text: German15001488Claude Opus 4.6
LMArena Text: Japanese14881460Claude Opus 4.6
LMArena Text: Korean14641460Claude Opus 4.6
LMArena Text: French15121503Claude Opus 4.6
LMArena Text: Spanish15101462Claude Opus 4.6
LMArena Text: Russian14971469Claude Opus 4.6
LMArena Text: Polish14991466Claude Opus 4.6
LMArena Text: multi-turn15111472Claude Opus 4.6
LMArena Text: longer queries15181483Claude Opus 4.6
LMArena Text: writing and language15021469Claude Opus 4.6
LMArena Text: entertainment and media14921447Claude Opus 4.6
LMArena Vision: English1324Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: Chinese1370Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: reading text1326Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: humor1314Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: recognizing things1274Not measuredOnly Claude Opus 4.6 measured
LMArena Vision: creative writing1331Not measuredOnly Claude Opus 4.6 measured
LMArena Document1507Not measuredOnly Claude Opus 4.6 measured
Neither model has a result on the other 9 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 4.6 and GLM 5.3
BenchmarkClaude Opus 4.6GLM 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 4.6$0.018Fits in one request
GLM 5.3$0.0036Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.6$0.325Fits in one request
GLM 5.3$0.083Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.6$1.25Fits 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 Opus 4.6 and GLM 5.3
SpecificationAnthropic Claude Opus 4.6Z.ai GLM 5.3
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedFebruary 4, 2026August 18, 2026
Context window1M1.05M
Input $/Mtok$5.00$1.40
Output $/Mtok$25.00$4.40
Coding index57.270.6
Independent measurements behind it46
Cost per solved task$1.56$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.6 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 57.2 for Claude Opus 4.6. Pick Claude Opus 4.6 for work measured by LiveCodeBench (Vals AI) or WeirdML, where it leads.

Which is cheaper, Claude Opus 4.6 or GLM 5.3?

Claude Opus 4.6 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Per solved SWE-bench Verified task, Claude Opus 4.6 costs $1.56 and GLM 5.3 $0.36.

Which has the bigger context window, Claude Opus 4.6 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the Claude Opus 4.6 vs GLM 5.3 comparison based on?

68 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.6's coding index rests on 4 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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