Noometry

Model comparison

Claude Opus 4.7 vs GLM-4.6

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Last verified . 27 shared benchmarks.

Claude Opus 4.7 Anthropic

58.3

Rank #19 Confirmed

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Summary

  • They share 27 benchmarks with published results for both. Claude Opus 4.7 scores higher in 9 categories and GLM-4.6 in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Claude Opus 4.7 leads 53.8 to 23.7.
  • The biggest single-benchmark swing is Terminal-Bench: 80.2% for Claude Opus 4.7 and 24.5% for GLM-4.6.
  • GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
  • Claude Opus 4.7 accepts more context: 1M tokens versus 205K.
  • GLM-4.6 has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.7GLM-4.6
ProviderAnthropicZ.ai (Zhipu)
Noometry Index58.341.4
Released2026-04-142025-09-30
WeightsProprietaryOpen
Context window1M205K
Max output128K131K
Input $ / M tokens$5$0.60
Output $ / M tokens$25$2.20
Results tracked6629

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Category by category

Coding Claude Opus 4.7 leads

Claude Opus 4.7: 59.6 (#13), GLM-4.6: 40.1 (#148)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena WebDev15581340
SciCode54.5%38.4%
LMArena Coding15181449
ALE-Bench1,323340.82
SWE-bench Verified83.5%—
FrontierCode38.5%—
SWE-bench Verified (bash only)—55.4%
GSO44.1%—
WeirdML76.4%—
MirrorCode31.1%—

Agentic & Tool Use Claude Opus 4.7 leads

Claude Opus 4.7: 47.9 (#10), GLM-4.6: 32.3 (#66)

BenchmarkClaude Opus 4.7GLM-4.6
Terminal-Bench80.2%24.5%
APEX-Agents49.2%—
Berkeley Function Calling Leaderboard—72.4%
OSWorld 2.018.2%—
τ²-bench Banking40.2%—
PostTrainBench28.6%—
ExploitBench26.5%—
GBAEval43.8%—
GDP.pdf21%—
LMArena Search1233—
Vending-Bench 210,937—

Reasoning Claude Opus 4.7 leads

Claude Opus 4.7: 53.8 (#29), GLM-4.6: 23.7 (#172)

BenchmarkClaude Opus 4.7GLM-4.6
Kagi LLM Benchmark80.7%47.4%
CritPt12%1.1%
LMArena Hard Prompts15061440
ARC-AGI-275.8%—
SimpleBench61.7%—
NYT Connections (extended)39%—
ARC-AGI-193.5%—
Chess Puzzles30%—
Thematic Generalization72.8%—
EBR-Bench19%—
Mystery Game Puzzles28%—
DTBench94.7%—
LMCA52.2%—
Epoch Capabilities Index156.25—
ForecastBench60.3—

Math Claude Opus 4.7 leads

Claude Opus 4.7: 66.7 (#26), GLM-4.6: 39.1 (#111)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Math14991432
FrontierMath (Feb 2025 set)43.8%3.8%
FrontierMath Tier 4 (v1)22.9%2.1%
FrontierMath (Tiers 1-3)70.2%—
FrontierMath Tier 431.7%—
MathArena Final-Answer Competitions73.6%—
OTIS Mock AIME 2024-202597.8%—
ProofBench54%—

Knowledge Claude Opus 4.7 leads

Claude Opus 4.7: 62.6 (#23), GLM-4.6: 40.2 (#124)

BenchmarkClaude Opus 4.7GLM-4.6
Vectara Hallucination Rate12%9.5%
LMArena Expert15211431
GPQA Diamond90.2%—
Humanity's Last Exam36.2%—
SimpleQA Verified51.7%—

Multimodal Not comparable

Claude Opus 4.7: 41.2 (#38), GLM-4.6: —

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Vision1316—
Blueprint-Bench 224.5%—
Furniture Assembly33.3%—
LMArena Document1495—

Multilingual Claude Opus 4.7 leads

Claude Opus 4.7: 57.3 (#10), GLM-4.6: 53.5 (#66)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Non-English14801426
LMArena Chinese15311499
LMArena French15031459
LMArena German14951447
LMArena Japanese14721393
LMArena Korean14641400
LMArena Russian14941419
LMArena Spanish14951436

Instruction Following Claude Opus 4.7 leads

Claude Opus 4.7: 78.4 (#10), GLM-4.6: 74.3 (#98)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Instruction Following14981410

Long Context Claude Opus 4.7 leads

Claude Opus 4.7: 46.2 (#25), GLM-4.6: 43.4 (#94)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Longer Query15051422

Writing & Preference Claude Opus 4.7 leads

Claude Opus 4.7: 75.1 (#8), GLM-4.6: 61.1 (#90)

BenchmarkClaude Opus 4.7GLM-4.6
LMArena Text14901440
LMArena Creative Writing14861411
EQ-Bench Creative Writing19141411
LMArena Multi-Turn15051427
EQ-Bench 41311—

Frequently asked questions

Is Claude Opus 4.7 better than GLM-4.6?

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 41.4 on the Noometry Index. GLM-4.6 costs 10× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.7 or GLM-4.6?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Claude Opus 4.7 lists at $5 and $25.

Is Claude Opus 4.7 or GLM-4.6 better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 205K.

How many benchmarks do Claude Opus 4.7 and GLM-4.6 share?

27 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and GLM-4.6 has 29.

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