Compare · ModelsLive · 2 picked · head to head

Claude Opus 4.6 vs GLM 5

Side by side · benchmarks, pricing, and signals you can act on.

Winner summary

Claude Opus 4.6 wins 16 of 16 shared benchmarks. Leads in agentic · reasoning · arena.

Category leads
agentic·Claude Opus 4.6reasoning·Claude Opus 4.6arena·Claude Opus 4.6knowledge·Claude Opus 4.6general·Claude Opus 4.6math·Claude Opus 4.6coding·Claude Opus 4.6
Hype vs Reality
Claude Opus 4.6
#153 by perf·#5 by attention
DESERVED
GLM 5
#90 by perf·#3 by attention
DESERVED
Best value
8.2x better value than Claude Opus 4.6
Claude Opus 4.6
3.2 pts/$
$15.00/M
GLM 5
26.0 pts/$
$2.10/M
Vendor risk
Anthropic logo
Anthropic
$965.0B·Tier 1
Medium risk
z-ai logo
z-ai
private · undisclosed
Unknown
Head to head
Claude Opus 4.6GLM 5
APEX-Agents
Claude Opus 4.6 leads by +29.1
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Opus 4.6
46.3
GLM 5
17.2
ARC-AGI
Claude Opus 4.6 leads by +49.3
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Opus 4.6
94.0
GLM 5
44.7
ARC-AGI-2
Claude Opus 4.6 leads by +64.3
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Opus 4.6
69.2
GLM 5
4.9
Chatbot Arena Elo · Coding
Claude Opus 4.6 leads by +103.0
Claude Opus 4.6
1537.0
GLM 5
1434.0
Chatbot Arena Elo · Overall
Claude Opus 4.6 leads by +39.7
Claude Opus 4.6
1497.3
GLM 5
1457.6
Chess Puzzles
Claude Opus 4.6 leads by +7.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Opus 4.6
17.0
GLM 5
10.0
Cl Bench
Claude Opus 4.6 leads by +2.0
Claude Opus 4.6
20.7
GLM 5
18.7
FrontierMath-2025-02-28-Private
Claude Opus 4.6 leads by +24.3
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Claude Opus 4.6
40.7
GLM 5
16.4
FrontierMath-Tier-4-2025-07-01-Private
Claude Opus 4.6 leads by +20.8
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Claude Opus 4.6
22.9
GLM 5
2.1
GPQA diamond
Claude Opus 4.6 leads by +7.3
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Opus 4.6
91.0
GLM 5
83.8
OTIS Mock AIME 2024-2025
Claude Opus 4.6 leads by +14.5
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Opus 4.6
94.4
GLM 5
80.0
PostTrainBench
Claude Opus 4.6 leads by +9.3
Claude Opus 4.6
23.2
GLM 5
13.9
SimpleBench
Claude Opus 4.6 leads by +17.3
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Opus 4.6
61.1
GLM 5
43.8
SWE-Bench verified
Claude Opus 4.6 leads by +6.6
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability.
Claude Opus 4.6
78.7
GLM 5
72.1
Terminal Bench
Claude Opus 4.6 leads by +27.4
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency.
Claude Opus 4.6
79.8
GLM 5
52.4
WeirdML
Claude Opus 4.6 leads by +29.7
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.6
77.9
GLM 5
48.2
Full benchmark table
BenchmarkClaude Opus 4.6GLM 5
APEX-Agents
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
46.317.2
ARC-AGI
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
94.044.7
ARC-AGI-2
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
69.24.9
Chatbot Arena Elo · Coding
1537.01434.0
Chatbot Arena Elo · Overall
1497.31457.6
Chess Puzzles
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
17.010.0
Cl Bench
20.718.7
FrontierMath-2025-02-28-Private
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
40.716.4
FrontierMath-Tier-4-2025-07-01-Private
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
22.92.1
GPQA diamond
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
91.083.8
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
94.480.0
PostTrainBench
23.213.9
SimpleBench
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
61.143.8
SWE-Bench verified
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability.
78.772.1
Terminal Bench
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency.
79.852.4
WeirdML
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
77.948.2
Pricing · per 1M tokens · projected $/mo at 10M tokens
ModelInputOutputContextProjected $/mo
Anthropic logoClaude Opus 4.6$5.00$25.001.0M tokens (~500 books)$100.00
z-ai logoGLM 5$1.00$3.20205K tokens (~102 books)$15.50