Compare · ModelsLive · 2 picked · head to head
Claude Haiku 4.5 vs GLM 5.2
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 10/10 benchmarks
GLM 5.2 wins 10 of 10 shared benchmarks. Leads in agentic · reasoning · knowledge.
Category leads
agentic·GLM 5.2reasoning·GLM 5.2knowledge·GLM 5.2general·GLM 5.2math·GLM 5.2coding·GLM 5.2
Hype vs Reality
Attention vs performance
Claude Haiku 4.5
#247 by perf·#17 by attention
GLM 5.2
#75 by perf·#3 by attention
Best value
GLM 5.2
1.5x better value than Claude Haiku 4.5
Claude Haiku 4.5
11.2 pts/$
$3.00/M
GLM 5.2
16.5 pts/$
$3.40/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
z-ai
private · undisclosed
Head to head
10 benchmarks · 2 models
Claude Haiku 4.5GLM 5.2
APEX-Agents
GLM 5.2 leads by +36.3
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Haiku 4.5
8.9
GLM 5.2
45.2
ARC-AGI
GLM 5.2 leads by +29.3
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Haiku 4.5
47.7
GLM 5.2
77.0
ARC-AGI-2
GLM 5.2 leads by +18.8
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Haiku 4.5
4.0
GLM 5.2
22.8
Chess Puzzles
GLM 5.2 leads by +14.7
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Haiku 4.5
3.2
GLM 5.2
17.9
Dtbench
GLM 5.2 leads by +33.3
Claude Haiku 4.5
56.0
GLM 5.2
89.3
GPQA diamond
GLM 5.2 leads by +31.2
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Haiku 4.5
61.6
GLM 5.2
92.9
Lmca
GLM 5.2 leads by +17.5
Claude Haiku 4.5
36.4
GLM 5.2
53.9
OTIS Mock AIME 2024-2025
GLM 5.2 leads by +19.8
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Haiku 4.5
66.6
GLM 5.2
86.4
SimpleQA Verified
GLM 5.2 leads by +28.3
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Haiku 4.5
5.9
GLM 5.2
34.2
WeirdML
GLM 5.2 leads by +24.7
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Haiku 4.5
45.4
GLM 5.2
70.1
Full benchmark table
| Benchmark | Claude Haiku 4.5 | GLM 5.2 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 8.9 | 45.2 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 47.7 | 77.0 |
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. | 4.0 | 22.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 3.2 | 17.9 |
Dtbench | 56.0 | 89.3 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 61.6 | 92.9 |
Lmca | 36.4 | 53.9 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 66.6 | 86.4 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 5.9 | 34.2 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 45.4 | 70.1 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.00 | $5.00 | 200K tokens (~100 books) | $20.00 | |
| $0.01 | $6.80 | 1.0M tokens (~524 books) | $17.05 |
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