Compare · ModelsLive · 3 picked · head to head
Claude Fable 5 vs GLM 5.2 vs Claude Opus 4.7
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Fable 5 wins on 13/13 benchmarks
Claude Fable 5 wins 13 of 13 shared benchmarks. Leads in arena · speed · agentic.
Category leads
arena·Claude Fable 5speed·Claude Fable 5agentic·Claude Fable 5knowledge·Claude Fable 5math·Claude Fable 5reasoning·Claude Fable 5coding·Claude Fable 5
Hype vs Reality
Attention vs performance
Claude Fable 5
#12 by perf·no signal
GLM 5.2
#11 by perf·no signal
Claude Opus 4.7
#65 by perf·no signal
Best value
GLM 5.2
7.9x better value than Claude Opus 4.7
Claude Fable 5
2.5 pts/$
$30.00/M
GLM 5.2
30.5 pts/$
$2.50/M
Claude Opus 4.7
3.9 pts/$
$15.00/M
Vendor risk
Who is behind the model
Anthropic
$380.0B·Tier 1
z-ai
private · undisclosed
Anthropic
$380.0B·Tier 1
Head to head
13 benchmarks · 3 models
Claude Fable 5GLM 5.2Claude Opus 4.7
Chatbot Arena Elo · Coding
Claude Fable 5 leads by +60.7
Claude Fable 5
1653.9
GLM 5.2
1593.3
Claude Opus 4.7
1556.9
Chatbot Arena Elo · Overall
Claude Fable 5 leads by +14.4
Claude Fable 5
1507.6
GLM 5.2
1471.0
Claude Opus 4.7
1493.1
Artificial Analysis · Agentic Index
Claude Fable 5 leads by +9.8
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Claude Fable 5
52.8
GLM 5.2
43.1
Artificial Analysis · Coding Index
Claude Fable 5 leads by +7.7
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Claude Fable 5
76.5
GLM 5.2
68.8
Artificial Analysis · Quality Index
Claude Fable 5 leads by +8.8
Claude Fable 5
59.9
GLM 5.2
51.1
APEX-Agents
Claude Fable 5 leads by +11.1
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Fable 5
45.0
Claude Opus 4.7
33.9
Chess Puzzles
Claude Fable 5 leads by +11.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Fable 5
41.0
Claude Opus 4.7
30.0
FrontierMath-Tier-4-v2-Private
Claude Fable 5 leads by +56.1
Claude Fable 5
87.8
Claude Opus 4.7
31.7
FrontierMath-Tiers-1-3-v2-Private
Claude Fable 5 leads by +16.8
Claude Fable 5
87.0
Claude Opus 4.7
70.2
OTIS Mock AIME 2024-2025
Claude Fable 5 leads by +1.9
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Fable 5
99.7
Claude Opus 4.7
97.8
SimpleBench
Claude Fable 5 leads by +22.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Fable 5
78.3
Claude Opus 4.7
55.5
SimpleQA Verified
Claude Fable 5 leads by +17.7
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Fable 5
68.3
Claude Opus 4.7
50.6
WeirdML
Claude Fable 5 leads by +11.4
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Fable 5
87.8
Claude Opus 4.7
76.4
Full benchmark table
| Benchmark | Claude Fable 5 | GLM 5.2 | Claude Opus 4.7 |
|---|---|---|---|
Chatbot Arena Elo · Coding | 1653.9 | 1593.3 | 1556.9 |
Chatbot Arena Elo · Overall | 1507.6 | 1471.0 | 1493.1 |
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 52.8 | 43.1 | — |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 76.5 | 68.8 | — |
Artificial Analysis · Quality Index | 59.9 | 51.1 | — |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 45.0 | — | 33.9 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 41.0 | — | 30.0 |
FrontierMath-Tier-4-v2-Private | 87.8 | — | 31.7 |
FrontierMath-Tiers-1-3-v2-Private | 87.0 | — | 70.2 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 99.7 | — | 97.8 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 78.3 | — | 55.5 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 68.3 | — | 50.6 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 87.8 | — | 76.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $10.00 | $50.00 | 1.0M tokens (~500 books) | $200.00 | |
| $1.00 | $4.00 | 1.0M tokens (~524 books) | $17.50 | |
| $5.00 | $25.00 | 1.0M tokens (~500 books) | $100.00 |