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Claude Opus 4.7 vs GLM 5.3

Updated October 21, 2026. 68 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Claude Opus 4.7 Replace GLM 5.3
Claude Opus 4.7Anthropic
Coding
51.1
Overall
40.7
Output $/M
$25.00
44
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
23
vs boards won of 68 shared, 1 tied

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 51.1 for Claude Opus 4.7.

Pick Claude Opus 4.7 when what matters is LiveCodeBench (Vals AI) or WeirdML, where it leads.

Model AClaude Opus 4.7Anthropic51.1/1001 independent benchmark$2.95 per solved taskModel BGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task
Shared results
68
Claude Opus 4.7 only
33
GLM 5.3 only
32
Like-for-like categories
7 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 4.7 51.1 from 1 benchmark, GLM 5.3 73.1 from 2 benchmarks.
Widest category
Agentic work, 11.1 points apart on average over 7 shared benchmarks.
Widest single gap
Vending-Bench 2: Claude Opus 4.7 $10,937 against GLM 5.3 $8,164, 17.3 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.7 $25.00 against GLM 5.3 $1.40 per million output tokens, 17.9x apart.
Context window
Claude Opus 4.7 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 4.7BarsGLM 5.3Ties
Coding410
Intelligence11
Agentic work25
Reasoning531 tie
Math60
Knowledge90
General174
All shared boards44231 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index410
Coding benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev15581621GLM 5.3
AA Coding Index73.674.8GLM 5.3
LiveCodeBench (Vals AI)85.1%80.5%Claude Opus 4.7
SWE-bench Verified (Vals AI)82.0%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)71.0%78.1%GLM 5.3
Code migration (Vals AI)43.9%44.2%GLM 5.3
ProgramBench (Vals AI)0.0%1.5%GLM 5.3
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)68.5%71.5%GLM 5.3
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)54.5%Not measuredOnly Claude Opus 4.7 measured
Terminal-Bench80.2%Not measuredOnly Claude Opus 4.7 measured
FrontierCode38.5%40.1%GLM 5.3
FrontierSWENot measured30.2%Only GLM 5.3 measured
GSO-Bench44.1%Not measuredOnly Claude Opus 4.7 measured
WeirdML76.4%75.4%Claude Opus 4.7
MirrorCode31.1%Not measuredOnly Claude Opus 4.7 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding15191497Claude Opus 4.7
LMArena Text: software and IT15161493Claude Opus 4.7
LMArena WebDev: React15601641GLM 5.3
LMArena WebDev: HTML15541595GLM 5.3
LMArena WebDev: from an image1577Not measuredOnly Claude Opus 4.7 measured
Neither model has a result on the other 10 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
AA Intelligence Index40.744.8GLM 5.3
Epoch Capabilities Index156.8156.0Claude Opus 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index25
Agentic work benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
AA Agentic Index38.653.1GLM 5.3
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1255Only 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)51.5%55.8%GLM 5.3
Tax Agent Bench (Vals AI)23.7%39.7%GLM 5.3
Legal Research Bench (Vals AI)38.5%49.0%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)63.7%56.3%Claude Opus 4.7
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom88.6%Not measuredOnly Claude Opus 4.7 measured
APEX-Agents49.2%56.6%GLM 5.3
OSWorld 2.018.2%Not measuredOnly Claude Opus 4.7 measured
PostTrainBench28.6%Not measuredOnly Claude Opus 4.7 measured
Vending-Bench 2$10,937$8,164Claude Opus 4.7
LMArena Search1230Not measuredOnly Claude Opus 4.7 measured
Neither model has a result on the other 19 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index53
Reasoning benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
GPQA Diamond90.2%88.1%Claude Opus 4.7
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
MMMU Pro (Vals AI)85.5%Not measuredOnly Claude Opus 4.7 measured
GPQA Diamond (Artificial Analysis run)91.4%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%42.3%Tie
GPQA Diamond (Epoch AI run)90.2%90.9%GLM 5.3
ARC-AGI93.5%Not measuredOnly Claude Opus 4.7 measured
ARC-AGI-275.8%Not measuredOnly Claude Opus 4.7 measured
SimpleBench61.7%Not measuredOnly Claude Opus 4.7 measured
Chess puzzles41.0%37.0%Claude Opus 4.7
Mystery game puzzles28.0%33.0%GLM 5.3
EBR-bench19.0%Not measuredOnly Claude Opus 4.7 measured
LMArena Text: hard prompts15061490Claude Opus 4.7
LMArena Text: expert15211514Claude Opus 4.7
LMArena Text: hard prompts in English15081496Claude Opus 4.7
LMArena Vision: diagrams1336Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: homework1337Not measuredOnly Claude Opus 4.7 measured
Neither model has a result on the other 3 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index60
Math benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
AIME96.3%Not measuredOnly Claude Opus 4.7 measured
OTIS Mock AIME97.8%91.1%Claude Opus 4.7
FrontierMath, tiers 1 to 370.2%68.8%Claude Opus 4.7
FrontierMath, tier 431.7%29.3%Claude Opus 4.7
ProofBench54.0%49.0%Claude Opus 4.7
LMArena Text: math14981495Claude Opus 4.7
LMArena Text: mathematical work15091497Claude Opus 4.7
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 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
MMLU Pro89.9%86.8%Claude Opus 4.7
LegalBench (Vals AI)85.3%84.8%Claude Opus 4.7
CaseLaw (Vals AI)68.4%Not measuredOnly Claude Opus 4.7 measured
CorpFin (Vals AI)66.1%Not measuredOnly Claude Opus 4.7 measured
TaxEval (Vals AI)75.3%72.4%Claude Opus 4.7
MortgageTax (Vals AI)70.3%Not measuredOnly Claude Opus 4.7 measured
MedCode (Vals AI)54.9%42.9%Claude Opus 4.7
SimpleQA Verified51.7%41.0%Claude Opus 4.7
LMArena Text: science15081498Claude Opus 4.7
LMArena Text: medicine and healthcare14951481Claude Opus 4.7
LMArena Text: legal and government15041473Claude Opus 4.7
LMArena Text: business and finance14901459Claude Opus 4.7
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 5.3Leader
Design Arena: websitesNot measured1295Only GLM 5.3 measured
Design Arena: UI componentsNot measured1314Only GLM 5.3 measured
Design Arena: game devNot measured1311Only GLM 5.3 measured
Design Arena: data vizNot measured1260Only GLM 5.3 measured
Design Arena: 3DNot measured1332Only GLM 5.3 measured
Design Arena: all code categoriesNot measured1304Only GLM 5.3 measured
Design Arena: SVGNot measured1292Only GLM 5.3 measured
Design Arena: ASCII artNot measured1213Only GLM 5.3 measured
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index174
General benchmarks for Claude Opus 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 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)56.1%Not measuredOnly Claude Opus 4.7 measured
MedScribe (Vals AI)83.0%88.8%GLM 5.3
Vals Multimodal Index67.4%Not measuredOnly Claude Opus 4.7 measured
IFBench58.6%Not measuredOnly Claude Opus 4.7 measured
Long context reasoning (AA-LCR)78.7%79.7%GLM 5.3
DTBench94.7%87.7%Claude Opus 4.7
LMCA52.2%55.5%GLM 5.3
Furniture assembly33.3%Not measuredOnly Claude Opus 4.7 measured
ExploitBench26.5%Not measuredOnly Claude Opus 4.7 measured
LMArena Text14901474Claude Opus 4.7
LMArena Text: instruction following14981479Claude Opus 4.7
LMArena Text: creative writing14861458Claude Opus 4.7
LMArena Vision1317Not measuredOnly Claude Opus 4.7 measured
LMArena Text: English14961482Claude Opus 4.7
LMArena Text: non-English14801459Claude Opus 4.7
LMArena Text: Chinese15311532GLM 5.3
LMArena Text: German14951488Claude Opus 4.7
LMArena Text: Japanese14741460Claude Opus 4.7
LMArena Text: Korean14641460Claude Opus 4.7
LMArena Text: French15031503Claude Opus 4.7
LMArena Text: Spanish14941462Claude Opus 4.7
LMArena Text: Russian14941469Claude Opus 4.7
LMArena Text: Polish14891466Claude Opus 4.7
LMArena Text: multi-turn15051472Claude Opus 4.7
LMArena Text: longer queries15051483Claude Opus 4.7
LMArena Text: writing and language14921469Claude Opus 4.7
LMArena Text: entertainment and media14741447Claude Opus 4.7
LMArena Vision: English1322Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: Chinese1371Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: reading text1327Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: humor1309Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: recognizing things1278Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: creative writing1326Not measuredOnly Claude Opus 4.7 measured
LMArena Document1495Not measuredOnly Claude Opus 4.7 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 4.7 and GLM 5.3
BenchmarkClaude Opus 4.7GLM 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 4.7$0.018Fits 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 4.7$0.325Fits 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 4.7$1.25Fits 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 4.7 and GLM 5.3
SpecificationAnthropic Claude Opus 4.7Z.ai GLM 5.3
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedApril 16, 2026August 18, 2026
Context window1M1.05M
Input $/Mtok$5.00$0.079
Output $/Mtok$25.00$1.40
Coding index51.173.1
Independent measurements behind it12
Cost per solved task$2.95$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.7 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 51.1 for Claude Opus 4.7. Pick Claude Opus 4.7 when what matters is LiveCodeBench (Vals AI) or WeirdML, where it leads.

Which is cheaper, Claude Opus 4.7 or GLM 5.3?

Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 5.3 costs $0.079 and $1.40. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and GLM 5.3 $0.36.

Which has the bigger context window, Claude Opus 4.7 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the Claude Opus 4.7 vs GLM 5.3 comparison based on?

68 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.7's coding index rests on 1 independent benchmark 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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