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Claude Opus 4.1 vs GLM 4.7

Claude Opus 4.1 leads GLM 4.7 on the Artificial Analysis Intelligence Index, 22.8 to 22.2, and GLM 4.7 costs 34.1x less per output token ($2.20 vs $75.00 per million). Updated October 22, 2026; 54 benchmarks have a result for both.

Replace Claude Opus 4.1 Replace GLM 4.7
Claude Opus 4.1Anthropic
Coding
No data
Overall
50.2
Output $/M
$75.00
23
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
31
vs boards won of 54 shared

Claude Opus 4.1 or GLM 4.7: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding.

Pick Claude Opus 4.1 for work measured by Terminal-Bench or LMArena Text: coding, where it leads.

Model AClaude Opus 4.1AnthropicNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
54
Claude Opus 4.1 only
19
GLM 4.7 only
27
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 4.1 not benchmarked, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Math, 8.2 points apart on average over 5 shared benchmarks.
Widest single gap
LiveCodeBench (Vals AI): Claude Opus 4.1 66.5% against GLM 4.7 82.2%, 15.8 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.1 $75.00 against GLM 4.7 $2.20 per million output tokens, 34.1x apart.
Context window
Claude Opus 4.1 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 4.1BarsGLM 4.7Ties
Coding33
Intelligence20
Reasoning24
Math32
Knowledge45
Web design26
General711
All shared boards2331
CodingLike-for-likeSix of these boards feed the Vibe Coding Index33
Coding benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
LMArena WebDev13901435GLM 4.7
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)66.5%82.2%GLM 4.7
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured31.8%Only GLM 4.7 measured
Terminal-Bench38.0%33.4%Claude Opus 4.1
WeirdML45.9%Not measuredOnly Claude Opus 4.1 measured
Cybench42.0%Not measuredOnly Claude Opus 4.1 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding14791454Claude Opus 4.1
LMArena Text: software and IT14561453Claude Opus 4.1
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTML13991447GLM 4.7
SciCode4.6%Not measuredOnly Claude Opus 4.1 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
AA Intelligence Index22.822.2Claude Opus 4.1
Epoch Capabilities Index144.1143.6Claude Opus 4.1
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1112Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench TelecomNot measured95.9%Only GLM 4.7 measured
METR time horizons66.8%Not measuredOnly Claude Opus 4.1 measured
GDPval43.6%Not measuredOnly Claude Opus 4.1 measured
DeepResearch Bench48.3%Not measuredOnly Claude Opus 4.1 measured
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
Reasoning benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
GPQA Diamond76.3%80.0%GLM 4.7
MMMU Pro (Vals AI)77.5%Not measuredOnly Claude Opus 4.1 measured
GPQA Diamond (Artificial Analysis run)Not measured85.9%Only GLM 4.7 measured
Humanity's Last Exam (Artificial Analysis run)Not measured27.4%Only GLM 4.7 measured
GPQA Diamond (Epoch AI run)77.3%83.3%GLM 4.7
SimpleBench60.0%47.7%Claude Opus 4.1
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
Mystery game puzzles21.0%Not measuredOnly Claude Opus 4.1 measured
EBR-bench7.9%Not measuredOnly Claude Opus 4.1 measured
VPCT35.0%Not measuredOnly Claude Opus 4.1 measured
LMArena Text: hard prompts14421443GLM 4.7
LMArena Text: expert14381424Claude Opus 4.1
LMArena Text: hard prompts in English14511452GLM 4.7
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index32
Math benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
AIME78.2%93.3%GLM 4.7
MATH 500 (Vals AI)95.4%Not measuredOnly Claude Opus 4.1 measured
MGSM (Vals AI)94.4%88.2%Claude Opus 4.1
AA Math IndexNot measured95.0Only GLM 4.7 measured
AIME 2025 (Artificial Analysis run)Not measured95.0%Only GLM 4.7 measured
OTIS Mock AIME68.9%83.3%GLM 4.7
FrontierMath, tiers 1 to 312.6%Not measuredOnly Claude Opus 4.1 measured
FrontierMath, tier 42.4%Not measuredOnly Claude Opus 4.1 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14311424Claude Opus 4.1
LMArena Text: mathematical work14341411Claude Opus 4.1
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index45
Knowledge benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
MMLU Pro87.9%82.7%Claude Opus 4.1
LegalBench (Vals AI)83.5%83.4%Claude Opus 4.1
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)73.7%68.8%Claude Opus 4.1
MortgageTax (Vals AI)61.1%Not measuredOnly Claude Opus 4.1 measured
MedQA (Vals AI)93.6%93.7%GLM 4.7
MedCode (Vals AI)47.2%32.8%Claude Opus 4.1
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14221457GLM 4.7
LMArena Text: medicine and healthcare14161469GLM 4.7
LMArena Text: legal and government14211444GLM 4.7
LMArena Text: business and finance14061429GLM 4.7
FACTS Benchmark Suite40.4%Not measuredOnly Claude Opus 4.1 measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
Design Arena: websites11761225GLM 4.7
Design Arena: UI components11621196GLM 4.7
Design Arena: game dev11711188GLM 4.7
Design Arena: data viz11661195GLM 4.7
Design Arena: 3D11521190GLM 4.7
Design Arena: all code categories11701216GLM 4.7
Design Arena: SVG11521142Claude Opus 4.1
Design Arena: ASCII art11671165Claude Opus 4.1
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index711
General benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 4.7Leader
SAGE (Vals AI)31.4%Not measuredOnly Claude Opus 4.1 measured
MedScribe (Vals AI)73.9%68.6%Claude Opus 4.1
IFBenchNot measured67.9%Only GLM 4.7 measured
Long context reasoning (AA-LCR)Not measured71.0%Only GLM 4.7 measured
DTBench80.0%Not measuredOnly Claude Opus 4.1 measured
LMCA37.1%Not measuredOnly Claude Opus 4.1 measured
Creative writing (Lech Mazur)84.7%Not measuredOnly Claude Opus 4.1 measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14191435GLM 4.7
LMArena Text: instruction following14351412Claude Opus 4.1
LMArena Text: creative writing14121401Claude Opus 4.1
LMArena Text: English14331453GLM 4.7
LMArena Text: non-English14051417GLM 4.7
LMArena Text: Chinese14271495GLM 4.7
LMArena Text: German14131426GLM 4.7
LMArena Text: Japanese13771439GLM 4.7
LMArena Text: Korean13811399GLM 4.7
LMArena Text: French14331432Claude Opus 4.1
LMArena Text: Spanish14481434Claude Opus 4.1
LMArena Text: Russian14221423GLM 4.7
LMArena Text: Polish14131435GLM 4.7
LMArena Text: multi-turn14441446GLM 4.7
LMArena Text: longer queries14551432Claude Opus 4.1
LMArena Text: writing and language14181411Claude Opus 4.1
LMArena Text: entertainment and media13981400GLM 4.7
Neither model has a result on the other 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 4.1 and GLM 4.7
BenchmarkClaude Opus 4.1GLM 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 4.1$0.052Fits 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 4.1$0.975Fits 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 4.1$3.75Exceeds 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 4.1 and GLM 4.7
SpecificationAnthropic Claude Opus 4.1Z.ai GLM 4.7
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedAugust 5, 2025December 22, 2025
Context window200K205K
Input $/Mtok$15.00$0.60
Output $/Mtok$75.00$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it12
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.1 better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding. Pick Claude Opus 4.1 for work measured by Terminal-Bench or LMArena Text: coding, where it leads.

Which is cheaper, Claude Opus 4.1 or GLM 4.7?

Claude Opus 4.1 costs $15.00 per million input tokens and $75.00 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, Claude Opus 4.1 or GLM 4.7?

GLM 4.7, with 205K tokens against 200K.

How many benchmarks is the Claude Opus 4.1 vs GLM 4.7 comparison based on?

54 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.1's coding index rests on 1 independent benchmark 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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