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Claude Opus 4.1 vs GLM 5.2

GLM 5.2 leads Claude Opus 4.1 on the Artificial Analysis Intelligence Index, 33.7 to 22.8, and GLM 5.2 costs 17.0x less per output token ($4.40 vs $75.00 per million). Updated October 21, 2026; 57 benchmarks have a result for both.

Replace Claude Opus 4.1 Replace GLM 5.2
Claude Opus 4.1Anthropic
Coding
No data
Overall
50.2
Output $/M
$75.00
5
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
52
vs boards won of 57 shared

Claude Opus 4.1 or GLM 5.2: which to pick

Only GLM 5.2 has a coding score, 61.1 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 SimpleBench or Mystery game puzzles, where it leads.

Model AClaude Opus 4.1AnthropicNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved task
Shared results
57
Claude Opus 4.1 only
16
GLM 5.2 only
41
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 5.2 61.1 from 4 benchmarks.
Widest category
Math, 21.8 points apart on average over 5 shared benchmarks.
Widest single gap
FrontierMath, tiers 1 to 3: Claude Opus 4.1 12.6% against GLM 5.2 59.2%, 46.6 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.1 $75.00 against GLM 5.2 $4.40 per million output tokens, 17.0x apart.
Context window
Claude Opus 4.1 takes 200K tokens, GLM 5.2 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 4.1BarsGLM 5.2Ties
Coding06
Intelligence02
Reasoning26
Math05
Knowledge35
Web design08
General020
All shared boards552
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
LMArena WebDev13901604GLM 5.2
AA Coding IndexNot measured68.8Only GLM 5.2 measured
LiveCodeBench (Vals AI)66.5%69.5%GLM 5.2
SWE-bench Verified (Vals AI)Not measured82.8%Only GLM 5.2 measured
Vibe Code Bench (Vals AI)Not measured64.0%Only GLM 5.2 measured
Code migration (Vals AI)Not measured37.9%Only GLM 5.2 measured
ProgramBench (Vals AI)Not measured0.5%Only GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured67.8%Only GLM 5.2 measured
SkillsBench (Vals AI)Not measured45.1%Only GLM 5.2 measured
SciCodeNot measured51.2%Only GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured50.8%Only GLM 5.2 measured
Terminal-Bench38.0%Not measuredOnly Claude Opus 4.1 measured
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdML45.9%70.1%GLM 5.2
Cybench42.0%Not measuredOnly Claude Opus 4.1 measured
ALE-BenchNot measured1047.0Only GLM 5.2 measured
LMArena Text: coding14791487GLM 5.2
LMArena Text: software and IT14561486GLM 5.2
LMArena WebDev: ReactNot measured1617Only GLM 5.2 measured
LMArena WebDev: HTML13991565GLM 5.2
SciCode4.6%Not measuredOnly Claude Opus 4.1 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
AA Intelligence Index22.833.7GLM 5.2
Epoch Capabilities Index144.1151.9GLM 5.2
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
Agentic work benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
AA Agentic IndexNot measured38.4Only GLM 5.2 measured
Design Arena: agents building web appsNot measured1222Only GLM 5.2 measured
Design Arena: agents building full-stack appsNot measured1241Only GLM 5.2 measured
Design Arena: agents building mobile appsNot measured1160Only GLM 5.2 measured
Design Arena: agents building Android appsNot measured1178Only GLM 5.2 measured
Design Arena: agents building Godot gamesNot measured1142Only GLM 5.2 measured
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1175Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1195Only GLM 5.2 measured
SREBench (Vals AI)Not measured0.0%Only GLM 5.2 measured
Finance Agent (Vals AI)Not measured49.7%Only GLM 5.2 measured
Legal Research Bench (Vals AI)Not measured31.3%Only GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)Not measured61.5%Only GLM 5.2 measured
τ²-Bench TelecomNot measured99.1%Only GLM 5.2 measured
APEX-AgentsNot measured45.2%Only GLM 5.2 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
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2Not measured$8,314Only GLM 5.2 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index26
Reasoning benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
GPQA Diamond76.3%85.6%GLM 5.2
MMMU Pro (Vals AI)77.5%Not measuredOnly Claude Opus 4.1 measured
GPQA Diamond (Artificial Analysis run)Not measured89.5%Only GLM 5.2 measured
Humanity's Last Exam (Artificial Analysis run)Not measured41.1%Only GLM 5.2 measured
GPQA Diamond (Epoch AI run)77.3%91.9%GLM 5.2
ARC-AGINot measured77.0%Only GLM 5.2 measured
ARC-AGI-2Not measured22.8%Only GLM 5.2 measured
SimpleBench60.0%58.8%Claude Opus 4.1
Chess puzzlesNot measured22.0%Only GLM 5.2 measured
Mystery game puzzles21.0%19.0%Claude Opus 4.1
EBR-bench7.9%9.5%GLM 5.2
VPCT35.0%Not measuredOnly Claude Opus 4.1 measured
LMArena Text: hard prompts14421481GLM 5.2
LMArena Text: expert14381487GLM 5.2
LMArena Text: hard prompts in English14511481GLM 5.2
Kagi LLM BenchmarkNot measured62.6%Only GLM 5.2 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
AIME78.2%Not measuredOnly Claude Opus 4.1 measured
MATH 500 (Vals AI)95.4%Not measuredOnly Claude Opus 4.1 measured
MGSM (Vals AI)94.4%Not measuredOnly Claude Opus 4.1 measured
OTIS Mock AIME68.9%86.4%GLM 5.2
FrontierMath, tiers 1 to 312.6%59.2%GLM 5.2
FrontierMath, tier 42.4%29.3%GLM 5.2
ProofBenchNot measured35.0%Only GLM 5.2 measured
LMArena Text: math14311483GLM 5.2
LMArena Text: mathematical work14341489GLM 5.2
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index35
Knowledge benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
MMLU Pro87.9%86.7%Claude Opus 4.1
LegalBench (Vals AI)83.5%84.1%GLM 5.2
CorpFin (Vals AI)Not measured66.1%Only GLM 5.2 measured
TaxEval (Vals AI)73.7%73.3%Claude Opus 4.1
MortgageTax (Vals AI)61.1%Not measuredOnly Claude Opus 4.1 measured
MedQA (Vals AI)93.6%Not measuredOnly Claude Opus 4.1 measured
MedCode (Vals AI)47.2%40.8%Claude Opus 4.1
SimpleQA VerifiedNot measured34.2%Only GLM 5.2 measured
LMArena Text: science14221489GLM 5.2
LMArena Text: medicine and healthcare14161475GLM 5.2
LMArena Text: legal and government14211486GLM 5.2
LMArena Text: business and finance14061451GLM 5.2
FACTS Benchmark Suite40.4%Not measuredOnly Claude Opus 4.1 measured
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
Design Arena: websites11771285GLM 5.2
Design Arena: UI components11651296GLM 5.2
Design Arena: game dev11711268GLM 5.2
Design Arena: data viz11661285GLM 5.2
Design Arena: 3D11531288GLM 5.2
Design Arena: all code categories11711288GLM 5.2
Design Arena: SVG11521214GLM 5.2
Design Arena: ASCII art11671190GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for Claude Opus 4.1 and GLM 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
SAGE (Vals AI)31.4%Not measuredOnly Claude Opus 4.1 measured
MedScribe (Vals AI)73.9%83.5%GLM 5.2
IFBenchNot measured73.3%Only GLM 5.2 measured
Long context reasoning (AA-LCR)Not measured78.3%Only GLM 5.2 measured
DTBench80.0%93.6%GLM 5.2
LMCA37.1%45.8%GLM 5.2
Creative writing (Lech Mazur)84.7%Not measuredOnly Claude Opus 4.1 measured
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
LMArena Text14191470GLM 5.2
LMArena Text: instruction following14351466GLM 5.2
LMArena Text: creative writing14121463GLM 5.2
LMArena Text: English14331475GLM 5.2
LMArena Text: non-English14051459GLM 5.2
LMArena Text: Chinese14271522GLM 5.2
LMArena Text: German14131470GLM 5.2
LMArena Text: Japanese13771450GLM 5.2
LMArena Text: Korean13811440GLM 5.2
LMArena Text: French14331479GLM 5.2
LMArena Text: Spanish14481478GLM 5.2
LMArena Text: Russian14221468GLM 5.2
LMArena Text: Polish14131456GLM 5.2
LMArena Text: multi-turn14441469GLM 5.2
LMArena Text: longer queries14551480GLM 5.2
LMArena Text: writing and language14181463GLM 5.2
LMArena Text: entertainment and media13981450GLM 5.2
Neither model has a result on the other 21 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 5.2
BenchmarkClaude Opus 4.1GLM 5.2Leader
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 5.2$0.0036Fits in one request

GLM 5.2 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.1$0.975Fits in one request
GLM 5.2$0.083Fits in one request

GLM 5.2 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.1$3.75Exceeds the context window
GLM 5.2$0.324Fits in one request

GLM 5.2 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 5.2
SpecificationAnthropic Claude Opus 4.1Z.ai GLM 5.2
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedAugust 5, 2025June 16, 2026
Context window200K1.05M
Input $/Mtok$15.00$1.40
Output $/Mtok$75.00$4.40
Coding indexNot benchmarked61.1
Independent measurements behind it14
Cost per solved taskNot computable$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.1 better than GLM 5.2 for coding?

Only GLM 5.2 has a coding score, 61.1 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 SimpleBench or Mystery game puzzles, where it leads.

Which is cheaper, Claude Opus 4.1 or GLM 5.2?

Claude Opus 4.1 costs $15.00 per million input tokens and $75.00 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, Claude Opus 4.1 or GLM 5.2?

GLM 5.2, with 1.05M tokens against 200K.

How many benchmarks is the Claude Opus 4.1 vs GLM 5.2 comparison based on?

57 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.1's coding index rests on 1 independent benchmark and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.

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