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Claude Opus 4.7 vs GLM 5.3 Flash

Claude Opus 4.7 leads GLM 5.3 Flash on coding, 67.5 to 57.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 50.0x less per output token ($0.50 vs $25.00 per million). Updated October 22, 2026; 74 benchmarks have a result for both.

Replace Claude Opus 4.7 Replace GLM 5.3 Flash
Claude Opus 4.7Anthropic
Coding
67.5
Overall
71.6
Output $/M
$25.00
56
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
16
vs boards won of 74 shared, 2 tied

Claude Opus 4.7 or GLM 5.3 Flash: which to pick

Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6.

Pick GLM 5.3 Flash for output tokens 50.0x cheaper ($0.50 vs $25.00 per million) and a lower cost per solved task ($0.02 against $2.95).

Model AClaude Opus 4.7Anthropic67.5/1004 independent benchmarks$2.95 per solved taskModel BGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task
Shared results
74
Claude Opus 4.7 only
29
GLM 5.3 Flash only
28
Like-for-like categories
7 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 4.7 67.5 from 4 benchmarks, GLM 5.3 Flash 57.7 from 6 benchmarks.
Widest category
Math, 11.5 points apart on average over 6 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): Claude Opus 4.7 71.0% against GLM 5.3 Flash 30.8%, 40.2 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.7 $25.00 against GLM 5.3 Flash $0.50 per million output tokens, 50.0x apart.
Context window
Claude Opus 4.7 takes 1M tokens, GLM 5.3 Flash 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 5.3 FlashTies
Coding851 tie
Intelligence11
Agentic work15
Reasoning1211 tie
Math51
Knowledge61
General232
All shared boards56162 ties
CodingLike-for-likeSix of these boards feed the Vibe Coding Index85
Coding benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
LiveBench codingNot measured80.6%Only GLM 5.3 Flash measured
LMArena WebDev15581607GLM 5.3 Flash
AA Coding Index73.671.5Claude Opus 4.7
LiveCodeBench (Vals AI)85.1%80.5%Claude Opus 4.7
SWE-bench Verified (Vals AI)82.0%92.0%GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)Not measured37.4%Only GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)71.0%30.8%Claude Opus 4.7
Code migration (Vals AI)43.9%20.5%Claude Opus 4.7
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)Not measured52.5%Only GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)68.5%62.9%Claude Opus 4.7
Vibe Code Bench 1-100 (Vals AI)Not measured16.0%Only GLM 5.3 Flash measured
SkillsBench (Vals AI)Not measured40.2%Only GLM 5.3 Flash measured
CyberBench (Vals AI)Not measured67.9%Only GLM 5.3 Flash measured
SciCodeNot measured51.6%Only GLM 5.3 Flash 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%31.8%Claude Opus 4.7
FrontierSWENot measured18.1%Only GLM 5.3 Flash measured
GSO-Bench44.1%Not measuredOnly Claude Opus 4.7 measured
WeirdML76.4%Not measuredOnly Claude Opus 4.7 measured
MirrorCode31.1%Not measuredOnly Claude Opus 4.7 measured
ALE-BenchNot measured303.6Only GLM 5.3 Flash measured
LMArena Text: coding15191507Claude Opus 4.7
LMArena Text: software and IT15161500Claude Opus 4.7
LMArena WebDev: React15601624GLM 5.3 Flash
LMArena WebDev: HTML15541577GLM 5.3 Flash
LMArena WebDev: from an image15771578GLM 5.3 Flash
Neither model has a result on the other 11 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 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
AA Intelligence Index40.741.8GLM 5.3 Flash
Epoch Capabilities Index156.8153.1Claude Opus 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
Agentic work benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
AA Agentic Index38.650.9GLM 5.3 Flash
LiveBench researchNot measured70.1%Only GLM 5.3 Flash measured
LiveBench browserNot measured84.0%Only GLM 5.3 Flash measured
LiveBench long agenticNot measured28.4%Only GLM 5.3 Flash measured
SREBench (Vals AI)Not measured0.8%Only GLM 5.3 Flash measured
Finance Agent (Vals AI)51.5%57.9%GLM 5.3 Flash
Tax Agent Bench (Vals AI)23.7%31.6%GLM 5.3 Flash
Legal Research Bench (Vals AI)38.5%45.2%GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)Not measured10.4%Only GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)63.7%55.9%Claude Opus 4.7
Terminal-Bench-Science (Vals AI)Not measured2.9%Only GLM 5.3 Flash measured
τ²-Bench Telecom88.6%Not measuredOnly Claude Opus 4.7 measured
APEX-Agents49.2%52.8%GLM 5.3 Flash
OSWorld 2.018.2%Not measuredOnly Claude Opus 4.7 measured
PostTrainBench28.6%Not measuredOnly Claude Opus 4.7 measured
Vending-Bench 2$10,937Not measuredOnly Claude Opus 4.7 measured
LMArena Search1230Not measuredOnly Claude Opus 4.7 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index121
Reasoning benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
GPQA Diamond90.2%86.4%Claude Opus 4.7
LiveBench reasoningNot measured77.6%Only GLM 5.3 Flash measured
MMMU Pro (Vals AI)85.5%86.0%GLM 5.3 Flash
GPQA Diamond (Artificial Analysis run)91.4%91.2%Claude Opus 4.7
Humanity's Last Exam (Artificial Analysis run)42.3%39.9%Claude Opus 4.7
GPQA Diamond (Epoch AI run)90.2%90.2%Tie
ARC-AGI93.5%91.0%Claude Opus 4.7
ARC-AGI-275.8%65.8%Claude Opus 4.7
SimpleBench61.7%Not measuredOnly Claude Opus 4.7 measured
Chess puzzles41.0%21.0%Claude Opus 4.7
Mystery game puzzles28.0%8.0%Claude Opus 4.7
EBR-bench19.0%Not measuredOnly Claude Opus 4.7 measured
LMArena Text: hard prompts15061490Claude Opus 4.7
LMArena Text: expert15211511Claude Opus 4.7
LMArena Text: hard prompts in English15081488Claude Opus 4.7
LMArena Vision: diagrams13361317Claude Opus 4.7
LMArena Vision: homework13371318Claude Opus 4.7
Kagi LLM Benchmark80.7%Not measuredOnly Claude Opus 4.7 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index51
Math benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
AIME96.3%Not measuredOnly Claude Opus 4.7 measured
OTIS Mock AIME97.8%93.9%Claude Opus 4.7
FrontierMath, tiers 1 to 370.2%55.8%Claude Opus 4.7
FrontierMath, tier 431.7%17.1%Claude Opus 4.7
ProofBench54.0%21.0%Claude Opus 4.7
LMArena Text: math14981503GLM 5.3 Flash
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 index61
Knowledge benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
MMLU Pro89.9%86.1%Claude Opus 4.7
LegalBench (Vals AI)85.3%83.9%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%75.6%GLM 5.3 Flash
MortgageTax (Vals AI)70.3%Not measuredOnly Claude Opus 4.7 measured
MedCode (Vals AI)54.9%Not measuredOnly Claude Opus 4.7 measured
SimpleQA Verified51.7%Not measuredOnly Claude Opus 4.7 measured
LMArena Text: science15081484Claude Opus 4.7
LMArena Text: medicine and healthcare14951475Claude Opus 4.7
LMArena Text: legal and government15041473Claude Opus 4.7
LMArena Text: business and finance14901460Claude Opus 4.7
FACTS Benchmark Suite45.5%Not measuredOnly Claude Opus 4.7 measured
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 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
Design Arena: websitesNot measured1272Only GLM 5.3 Flash measured
Design Arena: UI componentsNot measured1316Only GLM 5.3 Flash measured
Design Arena: game devNot measured1278Only GLM 5.3 Flash measured
Design Arena: data vizNot measured1262Only GLM 5.3 Flash measured
Design Arena: 3DNot measured1316Only GLM 5.3 Flash measured
Design Arena: all code categoriesNot measured1282Only GLM 5.3 Flash measured
Design Arena: SVGNot measured1285Only GLM 5.3 Flash measured
Design Arena: ASCII artNot measured1267Only GLM 5.3 Flash measured
LiveBench designNot measured77.2%Only GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index232
General benchmarks for Claude Opus 4.7 and GLM 5.3 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
Vals IndexNot measured50.9%Only GLM 5.3 Flash measured
LiveBench data analysisNot measured76.4%Only GLM 5.3 Flash measured
SAGE (Vals AI)56.1%Not measuredOnly Claude Opus 4.7 measured
MedScribe (Vals AI)83.0%88.9%GLM 5.3 Flash
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%80.0%GLM 5.3 Flash
DTBench94.7%Not measuredOnly Claude Opus 4.7 measured
LMCA52.2%Not measuredOnly Claude Opus 4.7 measured
Surface Evolver BenchNot measured52.5%Only GLM 5.3 Flash measured
Furniture assembly33.3%Not measuredOnly Claude Opus 4.7 measured
ExploitBench26.5%Not measuredOnly Claude Opus 4.7 measured
LMArena Text14901471Claude Opus 4.7
LMArena Text: instruction following14981478Claude Opus 4.7
LMArena Text: creative writing14861441Claude Opus 4.7
LMArena Vision13171294Claude Opus 4.7
LMArena Text: English14961474Claude Opus 4.7
LMArena Text: non-English14801462Claude Opus 4.7
LMArena Text: Chinese15311531Claude Opus 4.7
LMArena Text: German14951462Claude Opus 4.7
LMArena Text: Japanese14741444Claude Opus 4.7
LMArena Text: Korean14641436Claude Opus 4.7
LMArena Text: French15031491Claude Opus 4.7
LMArena Text: Spanish14941475Claude Opus 4.7
LMArena Text: Russian14941465Claude Opus 4.7
LMArena Text: Polish14891450Claude Opus 4.7
LMArena Text: multi-turn15051469Claude Opus 4.7
LMArena Text: longer queries15051481Claude Opus 4.7
LMArena Text: writing and language14921455Claude Opus 4.7
LMArena Text: entertainment and media14741440Claude Opus 4.7
LMArena Vision: English13221295Claude Opus 4.7
LMArena Vision: Chinese13711363Claude Opus 4.7
LMArena Vision: reading text13271299Claude Opus 4.7
LMArena Vision: humor13091279Claude Opus 4.7
LMArena Vision: recognizing things1278Not measuredOnly Claude Opus 4.7 measured
LMArena Vision: creative writing13261270Claude Opus 4.7
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 Flash
BenchmarkClaude Opus 4.7GLM 5.3 FlashLeader
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 Flash$0.0004Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.7$0.325Fits in one request
GLM 5.3 Flash$0.0090Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.7$1.25Fits in one request
GLM 5.3 Flash$0.035Fits in one request

GLM 5.3 Flash 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 Flash
SpecificationAnthropic Claude Opus 4.7Z.ai GLM 5.3 Flash
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedApril 16, 2026August 26, 2026
Context window1M1.05M
Input $/Mtok$5.00$0.15
Output $/Mtok$25.00$0.50
Coding index67.557.7
Independent measurements behind it46
Cost per solved task$2.95$0.02
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.7 better than GLM 5.3 Flash for coding?

Pick Claude Opus 4.7 for coding: it scores 67.5 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6. Pick GLM 5.3 Flash for output tokens 50.0x cheaper ($0.50 vs $25.00 per million) and a lower cost per solved task ($0.02 against $2.95).

Which is cheaper, Claude Opus 4.7 or GLM 5.3 Flash?

Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and GLM 5.3 Flash $0.02.

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

GLM 5.3 Flash, with 1.05M tokens against 1M.

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

74 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.7's coding index rests on 4 independent benchmarks and GLM 5.3 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.

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