Objective comparison based on public benchmarks updated weekly: Intelligence Index, Chatbot Arena ELO, pricing and speed.
Overall winner (2026)
Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback)
4 of 5 criteria won
Anthropic
ELO Arena
1501
Intelligence Index
53.4
Coding Index
81.6
4 criteria won
View full profile →OpenAI
ELO Arena
1476
Intelligence Index
52.7
Coding Index
76.9
1 criterion won
View full profile →| Critério | Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) | GPT-6 Astra (max) |
|---|---|---|
| Chatbot Arena ELO | 1501 ✓ | 1476 |
| Intelligence Index (AA) | 53.4 ✓ | 52.7 |
| Coding Index (AA) | 81.6 ✓ | 76.9 |
| Input price ($/1M tok) | $10.00 | $10.00 |
| Output price ($/1M tok) | $50.00 | $50.00 |
| Context window | 1.0M tokens | 1.1M tokens ✓ |
| Speed (tokens/s) | 68 tok/s ✓ | 52 tok/s |
✓ = winner in this criterion • Source: Artificial Analysis, LMArena, official APIs • Updated weekly
Choosing between Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) and GPT-6 Astra (max) depends on your use case, budget and technical requirements. Below, a practical guide based on benchmark data and each model's specifications.
Anthropic · Text
OpenAI · Text
Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) wins in 4 of 5 criteria analyzed. Check the full table to choose based on your use case.
Data is aggregated from Artificial Analysis (Intelligence Index, Coding Index) and Chatbot Arena/LMArena (ELO). Pricing and specs come from official APIs. Updated weekly.
The Intelligence Index is an aggregate score from Artificial Analysis that combines multiple academic benchmarks (MMLU, GPQA, LiveBench, etc.) into a single rating. The higher the score, the more capable the model is at reasoning tasks.
Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) and GPT-6 Astra (max) have the same input price: $10/1M tokens. The actual cost difference depends on output pricing and your application's input/output ratio.
GPT-6 Astra (max) has the larger context window: 1.1M tokens vs 1.0M tokens. For long document analysis, extensive transcripts or full codebases, the larger context window is a decisive criterion.