Compare · ModelsLive · 3 picked · head to head
Claude Fable 5 vs Claude Opus 4.8 (Fast) vs GPT-5.5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Fable 5 wins on 24/28 benchmarks
Claude Fable 5 wins 24 of 28 shared benchmarks. Leads in speed · agentic · reasoning.
Category leads
speed·Claude Fable 5agentic·Claude Fable 5reasoning·Claude Fable 5arena·Claude Fable 5knowledge·GPT-5.5coding·Claude Fable 5general·Claude Fable 5math·Claude Fable 5
Hype vs Reality
Attention vs performance
Claude Fable 5
#30 by perf·#5 by attention
Claude Opus 4.8 (Fast)
no bench·#6 by attention
GPT-5.5
#64 by perf·#9 by attention
Best value
GPT-5.5
1.5x better value than Claude Fable 5
Claude Fable 5
2.3 pts/$
$30.00/M
Claude Opus 4.8 (Fast)
n/a
$30.00/M
GPT-5.5
3.3 pts/$
$17.50/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
Anthropic
$965.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
28 benchmarks · 3 models
Claude Fable 5Claude Opus 4.8 (Fast)GPT-5.5
Artificial Analysis · Agentic Index
Claude Fable 5 leads by +5.6
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
Claude Opus 4.8 (Fast)
47.2
GPT-5.5
44.9
Artificial Analysis · Coding Index
Claude Fable 5 leads by +1.6
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
Claude Opus 4.8 (Fast)
74.3
GPT-5.5
74.9
Artificial Analysis · Quality Index
Claude Fable 5 leads by +4.2
Claude Fable 5
59.9
Claude Opus 4.8 (Fast)
55.7
GPT-5.5
54.8
APEX-Agents
Claude Fable 5 leads by +8.5
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Fable 5
63.6
GPT-5.5
55.1
ARC-AGI
Claude Fable 5 leads by +3.5
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Fable 5
98.5
GPT-5.5
95.0
ARC-AGI-2
Claude Fable 5 leads by +4.2
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Fable 5
89.2
GPT-5.5
85.0
Chatbot Arena Elo · Overall
Claude Fable 5 leads by +30.8
Claude Fable 5
1507.6
GPT-5.5
1476.8
Chess Puzzles
GPT-5.5 leads by +16.8
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Fable 5
44.2
GPT-5.5
61.1
Deepswe
Claude Fable 5 leads by +2.9
Claude Fable 5
69.9
GPT-5.5
67.0
Dtbench
Claude Fable 5 leads by +4.0
Claude Fable 5
97.3
GPT-5.5
93.3
Ebr Bench
Claude Fable 5 leads by +5.2
Claude Fable 5
39.5
GPT-5.5
34.3
Frontiercode
Claude Fable 5 leads by +10.5
Claude Fable 5
53.5
GPT-5.5
43.0
FrontierMath-Tier-4-v2-Private
Claude Fable 5 leads by +17.7
Claude Fable 5
90.2
GPT-5.5
72.5
FrontierMath-Tiers-1-3-v2-Private
Claude Fable 5 leads by +1.8
Claude Fable 5
91.6
GPT-5.5
89.8
Furniture Assembly
GPT-5.5 leads by +11.9
Claude Fable 5
8.3
GPT-5.5
20.2
GPQA diamond
GPT-5.5 leads by +11.3
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Fable 5
84.5
GPT-5.5
95.8
GSO-Bench
Claude Fable 5 leads by +38.2
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
Claude Fable 5
78.4
GPT-5.5
40.2
Lmca
Claude Fable 5 leads by +8.1
Claude Fable 5
71.9
GPT-5.5
63.9
Mirrorcode
Claude Fable 5 leads by +46.7
Claude Fable 5
70.0
GPT-5.5
23.3
Mystery Game Puzzles
GPT-5.5 leads by +4.4
Claude Fable 5
47.1
GPT-5.5
51.5
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Fable 5
100.0
GPT-5.5
100.0
PostTrainBench
Claude Fable 5 leads by +13.4
Claude Fable 5
37.3
GPT-5.5
23.9
Proofbench
Claude Fable 5 leads by +45.0
Claude Fable 5
95.0
GPT-5.5
50.0
Remote Labor Index
Claude Fable 5 leads by +9.9
Claude Fable 5
16.1
GPT-5.5
6.3
SimpleBench
Claude Fable 5 leads by +15.5
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Fable 5
78.3
GPT-5.5
62.8
SimpleQA Verified
Claude Fable 5 leads by +7.7
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Fable 5
70.7
GPT-5.5
63.0
Surface Evolver Bench
Claude Fable 5 leads by +6.9
Claude Fable 5
95.0
GPT-5.5
88.1
WeirdML
Claude Fable 5 leads by +7.0
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Fable 5
91.9
GPT-5.5
84.9
Full benchmark table
| Benchmark | Claude Fable 5 | Claude Opus 4.8 (Fast) | GPT-5.5 |
|---|---|---|---|
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 | 47.2 | 44.9 |
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 | 74.3 | 74.9 |
Artificial Analysis · Quality Index | 59.9 | 55.7 | 54.8 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 63.6 | n/a | 55.1 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 98.5 | n/a | 95.0 |
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. | 89.2 | n/a | 85.0 |
Chatbot Arena Elo · Overall | 1507.6 | n/a | 1476.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 44.2 | n/a | 61.1 |
Deepswe | 69.9 | n/a | 67.0 |
Dtbench | 97.3 | n/a | 93.3 |
Ebr Bench | 39.5 | n/a | 34.3 |
Frontiercode | 53.5 | n/a | 43.0 |
FrontierMath-Tier-4-v2-Private | 90.2 | n/a | 72.5 |
FrontierMath-Tiers-1-3-v2-Private | 91.6 | n/a | 89.8 |
Furniture Assembly | 8.3 | n/a | 20.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 84.5 | n/a | 95.8 |
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 78.4 | n/a | 40.2 |
Lmca | 71.9 | n/a | 63.9 |
Mirrorcode | 70.0 | n/a | 23.3 |
Mystery Game Puzzles | 47.1 | n/a | 51.5 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 100.0 | n/a | 100.0 |
PostTrainBench | 37.3 | n/a | 23.9 |
Proofbench | 95.0 | n/a | 50.0 |
Remote Labor Index | 16.1 | n/a | 6.3 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 78.3 | n/a | 62.8 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 70.7 | n/a | 63.0 |
Surface Evolver Bench | 95.0 | n/a | 88.1 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 91.9 | n/a | 84.9 |
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 | |
| $10.00 | $50.00 | 1.0M tokens (~500 books) | $200.00 | |
| $5.00 | $30.00 | 1.1M tokens (~525 books) | $112.50 |