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Claude Opus 4.5 vs GLM 4.7

GLM 4.7 leads Claude Opus 4.5 on coding, 55.1 to 44.0 on the Vibe Coding Index, and GLM 4.7 costs 11.4x less per output token ($2.20 vs $25.00 per million). Updated October 22, 2026; 76 benchmarks have a result for both.

Replace Claude Opus 4.5 Replace GLM 4.7
Claude Opus 4.5Anthropic
Coding
44.0
Overall
55.7
Output $/M
$25.00
66
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
10
vs boards won of 76 shared

Claude Opus 4.5 or GLM 4.7: which to pick

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 44.0 for Claude Opus 4.5, though on 2 benchmarks against 3.

Pick Claude Opus 4.5 for work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Model AClaude Opus 4.5Anthropic44.0/1003 independent benchmarks$1.37 per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
76
Claude Opus 4.5 only
32
GLM 4.7 only
5
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 4.5 44.0 from 3 benchmarks, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Agentic work, 10.9 points apart on average over 5 shared benchmarks.
Widest single gap
ProofBench: Claude Opus 4.5 36.0% against GLM 4.7 6.0%, 30.0 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.5 $25.00 against GLM 4.7 $2.20 per million output tokens, 11.4x apart.
Context window
Claude Opus 4.5 takes 200K tokens, GLM 4.7 205K.

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 4.5BarsGLM 4.7Ties
Coding91
Intelligence20
Agentic work41
Reasoning81
Math62
Knowledge112
Web design80
General183
All shared boards6610
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
Coding benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
SWE-bench Verified79.2%Not measuredOnly Claude Opus 4.5 measured
SWE-bench (full test set)52.6%Not measuredOnly Claude Opus 4.5 measured
SWE-bench Multilingual70.7%Not measuredOnly Claude Opus 4.5 measured
LMArena WebDev14941435Claude Opus 4.5
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)83.7%82.2%Claude Opus 4.5
SWE-bench Verified (Vals AI)76.4%69.4%Claude Opus 4.5
Vibe Code Bench (Vals AI)20.6%Not measuredOnly Claude Opus 4.5 measured
LiveCodeBench (Artificial Analysis run)87.1%89.4%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)47.0%31.8%Claude Opus 4.5
Terminal-Bench63.1%33.4%Claude Opus 4.5
GSO-Bench26.5%Not measuredOnly Claude Opus 4.5 measured
WeirdML63.7%Not measuredOnly Claude Opus 4.5 measured
Cybench82.0%Not measuredOnly Claude Opus 4.5 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding15031454Claude Opus 4.5
LMArena Text: software and IT14831453Claude Opus 4.5
LMArena WebDev: React14781420Claude Opus 4.5
LMArena WebDev: HTML15101447Claude Opus 4.5
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
AA Intelligence Index29.122.2Claude Opus 4.5
Epoch Capabilities Index150.1143.6Claude Opus 4.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index41
Agentic work benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
Design Arena: agents building web apps1159Not measuredOnly Claude Opus 4.5 measured
Design Arena: agents building full-stack apps11561050Claude Opus 4.5
Design Arena: agents building mobile apps11891105Claude Opus 4.5
Design Arena: agents building Android apps11761112Claude Opus 4.5
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom89.5%95.9%GLM 4.7
METR time horizons75.0%Not measuredOnly Claude Opus 4.5 measured
Remote Labor Index3.8%Not measuredOnly Claude Opus 4.5 measured
GDPval45.5%Not measuredOnly Claude Opus 4.5 measured
BALROG43.5%Not measuredOnly Claude Opus 4.5 measured
DeepResearch Bench54.8%Not measuredOnly Claude Opus 4.5 measured
OSWorld66.3%Not measuredOnly Claude Opus 4.5 measured
Vending-Bench 2$4,967$2,377Claude Opus 4.5
Berkeley Function Calling Leaderboard (BFCL)77.5%Not measuredOnly Claude Opus 4.5 measured
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
Reasoning benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
GPQA Diamond85.9%80.0%Claude Opus 4.5
MMMU Pro (Vals AI)83.0%Not measuredOnly Claude Opus 4.5 measured
GPQA Diamond (Artificial Analysis run)86.6%85.9%Claude Opus 4.5
Humanity's Last Exam (Artificial Analysis run)30.1%27.4%Claude Opus 4.5
GPQA Diamond (Epoch AI run)86.0%83.3%Claude Opus 4.5
ARC-AGI80.0%Not measuredOnly Claude Opus 4.5 measured
ARC-AGI-237.6%Not measuredOnly Claude Opus 4.5 measured
SimpleBench62.0%47.7%Claude Opus 4.5
Chess puzzles14.0%21.0%GLM 4.7
Mystery game puzzles22.0%Not measuredOnly Claude Opus 4.5 measured
EBR-bench14.3%Not measuredOnly Claude Opus 4.5 measured
VPCT40.0%Not measuredOnly Claude Opus 4.5 measured
LMArena Text: hard prompts14761443Claude Opus 4.5
LMArena Text: expert14871424Claude Opus 4.5
LMArena Text: hard prompts in English14821452Claude Opus 4.5
Kagi LLM Benchmark80.2%Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index62
Math benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
AIME95.4%93.3%Claude Opus 4.5
MGSM (Vals AI)95.2%88.2%Claude Opus 4.5
AA Math Index91.395.0GLM 4.7
AIME 2025 (Artificial Analysis run)91.3%95.0%GLM 4.7
OTIS Mock AIME86.1%83.3%Claude Opus 4.5
FrontierMath, tiers 1 to 334.4%Not measuredOnly Claude Opus 4.5 measured
FrontierMath, tier 44.9%Not measuredOnly Claude Opus 4.5 measured
ProofBench36.0%6.0%Claude Opus 4.5
LMArena Text: math14631424Claude Opus 4.5
LMArena Text: mathematical work14681411Claude Opus 4.5
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index112
Knowledge benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
MMLU Pro87.3%82.7%Claude Opus 4.5
LegalBench (Vals AI)84.6%83.4%Claude Opus 4.5
CaseLaw (Vals AI)62.6%54.9%Claude Opus 4.5
CorpFin (Vals AI)65.1%46.4%Claude Opus 4.5
TaxEval (Vals AI)74.9%68.8%Claude Opus 4.5
MortgageTax (Vals AI)68.7%Not measuredOnly Claude Opus 4.5 measured
MedQA (Vals AI)95.9%93.7%Claude Opus 4.5
MedCode (Vals AI)49.2%32.8%Claude Opus 4.5
MMLU Pro (Artificial Analysis run)89.5%85.6%Claude Opus 4.5
SimpleQA Verified45.7%32.2%Claude Opus 4.5
GeoBench75.0%Not measuredOnly Claude Opus 4.5 measured
LMArena Text: science14541457GLM 4.7
LMArena Text: medicine and healthcare14441469GLM 4.7
LMArena Text: legal and government14591444Claude Opus 4.5
LMArena Text: business and finance14481429Claude Opus 4.5
FACTS Benchmark Suite43.9%Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 2 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
Design Arena: websites12471225Claude Opus 4.5
Design Arena: UI components12351196Claude Opus 4.5
Design Arena: game dev12281188Claude Opus 4.5
Design Arena: data viz12431195Claude Opus 4.5
Design Arena: 3D12131190Claude Opus 4.5
Design Arena: all code categories12421216Claude Opus 4.5
Design Arena: SVG11801142Claude Opus 4.5
Design Arena: ASCII art11901165Claude Opus 4.5
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index183
General benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
SAGE (Vals AI)52.1%Not measuredOnly Claude Opus 4.5 measured
MedScribe (Vals AI)85.3%68.6%Claude Opus 4.5
IFBench58.0%67.9%GLM 4.7
Long context reasoning (AA-LCR)77.3%71.0%Claude Opus 4.5
DTBench89.9%Not measuredOnly Claude Opus 4.5 measured
LMCA44.5%Not measuredOnly Claude Opus 4.5 measured
CL-bench21.1%15.9%Claude Opus 4.5
Furniture assembly28.3%Not measuredOnly Claude Opus 4.5 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14511435Claude Opus 4.5
LMArena Text: instruction following14781412Claude Opus 4.5
LMArena Text: creative writing14451401Claude Opus 4.5
LMArena Text: English14611453Claude Opus 4.5
LMArena Text: non-English14381417Claude Opus 4.5
LMArena Text: Chinese14701495GLM 4.7
LMArena Text: German14491426Claude Opus 4.5
LMArena Text: Japanese14171439GLM 4.7
LMArena Text: Korean14241399Claude Opus 4.5
LMArena Text: French14711432Claude Opus 4.5
LMArena Text: Spanish14571434Claude Opus 4.5
LMArena Text: Russian14471423Claude Opus 4.5
LMArena Text: Polish14391435Claude Opus 4.5
LMArena Text: multi-turn14661446Claude Opus 4.5
LMArena Text: longer queries14791432Claude Opus 4.5
LMArena Text: writing and language14521411Claude Opus 4.5
LMArena Text: entertainment and media14341400Claude Opus 4.5
LMArena Document1462Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 4.5 and GLM 4.7
BenchmarkClaude Opus 4.5GLM 4.7Leader
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 4.5$0.018Fits in one request
GLM 4.7$0.0017Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.5$0.325Fits in one request
GLM 4.7$0.037Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.5$1.25Exceeds the context window
GLM 4.7$0.142Exceeds the context window

GLM 4.7 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 4.5 and GLM 4.7
SpecificationAnthropic Claude Opus 4.5Z.ai GLM 4.7
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedNovember 24, 2025December 22, 2025
Context window200K205K
Input $/Mtok$5.00$0.60
Output $/Mtok$25.00$2.20
Coding index44.055.1
Independent measurements behind it32
Cost per solved task$1.37$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.5 better than GLM 4.7 for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 44.0 for Claude Opus 4.5, though on 2 benchmarks against 3. Pick Claude Opus 4.5 for work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, Claude Opus 4.5 or GLM 4.7?

Claude Opus 4.5 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Per solved SWE-bench Verified task, Claude Opus 4.5 costs $1.37 and GLM 4.7 $0.29.

Which has the bigger context window, Claude Opus 4.5 or GLM 4.7?

GLM 4.7, with 205K tokens against 200K.

How many benchmarks is the Claude Opus 4.5 vs GLM 4.7 comparison based on?

76 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.5's coding index rests on 3 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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