{"slug": "november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far", "title": "November 2026 Claude Fable 5.2 Scores 65 on Artificial Analysis, Highest So Far", "summary": "Anthropic's Claude Fable 5.2 (Max, Default Fallback), released November 9, 2026, scored 65 on the Artificial Analysis Intelligence Index, well above the median of 40 for comparable models, according to Artificial Analysis. The reasoning model accepts text and image input, outputs text, carries a 1M-token context window, and generated 275M tokens during the index evaluation versus a median of 72.8M. Pricing is $8.00 per 1M input tokens (median: $2.00) and $40.00 per 1M output tokens (median: $10.00), with an average cost of $26.54 per task to evaluate the model on the Intelligence Index.", "body_md": "# Claude Fable 5.2 (Max, Default Fallback) Intelligence, Performance & Price Analysis\n\n### Model summary\n\n#### Performance\n\nClaude Fable 5.2 (Max, Default Fallback) is amongst the leading models in intelligence, but particularly expensive when comparing to other models of similar price. The model supports text and image input, outputs text, and has a 1M tokens context window.\n\nClaude Fable 5.2 (Max, Default Fallback) scores 65 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 40). When evaluating the Intelligence Index, it generated 275M tokens, which is very verbose in comparison to the median of 72.8M.\n\nPricing for Claude Fable 5.2 (Max, Default Fallback) is $8.00 per 1M input tokens (expensive, median: $2.00) and $40.00 per 1M output tokens (expensive, median: $10.00). On average, it costs $26.54 per task to evaluate Claude Fable 5.2 (Max, Default Fallback) on the Intelligence Index.\n\n| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. | \n|---|---|\n| Input modality | Supports: text and image | \n| Output modality | Supports: text | \n| Context window | 1M ~1500 A4 pages of size 12 Arial font | \n\nMetrics are compared against models of the same class:\n\n- Non-reasoning models → compared only with other non-reasoning models\n- Reasoning models → compared across both reasoning and non-reasoning\n- Open weights models → compared only with other open weights models of the same size class:\n- Tiny: ≤4B parameters\n- Small: 4B–40B parameters\n- Medium: 40B–150B parameters\n- Large: >150B parameters\n- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:\n- <$0.15 per 1M tokens\n- $0.15–$1 per 1M tokens\n- >$1 per 1M tokens\n\n### Time per Task\n\n## Intelligence\n\n### [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index)\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\n### Intelligence Index vs. Cost per Task\n\nMeasures the performance of models on specific capabilities and industries\n\n### [Artificial Analysis Finance & Accounting Index](https://artificialanalysis.ai/indexes/finance-and-accounting)\n\n### Evaluations\n\nAgentic knowledge work, Elo\n\nKnowledge\n\n1 - hallucination rate\n\nAgentic SaaS workflows\n\nAgentic scientific research workflows in a terminal\n\nAgentic software engineering in open-source repositories\n\nReasoning & knowledge\n\nProfessional document reasoning, All-pass\n\nAgentic coding & terminal use\n\nPhysics reasoning\n\nQuantitative analysis on spreadsheets & documents\n\nLegal agentic work, Hallucination-Gated All-Pass Rate\n\nAgentic business operations\n\nAgentic real-world work tasks, Elo\n\nAgentic interactive 3D experiences, Elo\n\nProduct and brand videos designed in code, Elo\n\nKubernetes incident root-cause analysis\n\nMedical long context reasoning\n\nVisual reasoning\n\nRich, varied, well-crafted language\n\nWell-structured, scannable, answer-first\n\nTruthful, acknowledges uncertainty, non-sycophantic\n\nUse of filler and generic language\n\n### AA-Briefcase v1.1\n\n### AA-Briefcase Elo\n\n### AA-Omniscience v1.1\n\n### AA-Omniscience Net Score\n\n## Token Use\n\n### Output Tokens per Intelligence Index Task\n\n## Cost\n\n### Cost per Intelligence Index Task\n\n### Cost to Run Artificial Analysis Intelligence Index\n\n### Pricing: Cache Hit, Input, and Output\n\n## Context Window\n\n### Context Window\n\n## Frequently Asked Questions\n\nCommon questions about Claude Fable 5.2 (Max, Default Fallback)\n\nClaude Fable 5.2 (Max, Default Fallback) was released on November 9, 2026.\n\nClaude Fable 5.2 (Max, Default Fallback) was created by Anthropic.\n\nClaude Fable 5.2 (Max, Default Fallback) scores 65 on the Artificial Analysis Intelligence Index, placing it well above average among other models in a similar price tier (median: 40).\n\nClaude Fable 5.2 (Max, Default Fallback) costs $8.00 per 1M input tokens (at the higher end, median: $2.00) and $40.00 per 1M output tokens (at the higher end, median: $10.00), based on Anthropic's API.\n\nClaude Fable 5.2 (Max, Default Fallback) costs $8.00 per 1M input tokens and $40.00 per 1M output tokens (based on Anthropic's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $5.74 per 1M tokens. Pricing may vary by provider. [Compare provider pricing](https://artificialanalysis.ai/models/claude-fable-5-2/providers)\n\nWhen evaluated on the Intelligence Index, Claude Fable 5.2 (Max, Default Fallback) generated 275M output tokens, which is at the higher end compared to other models in a similar price tier (median: 72.8M).\n\nYes, Claude Fable 5.2 (Max, Default Fallback) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\nClaude Fable 5.2 (Max, Default Fallback) supports text and image input.\n\nClaude Fable 5.2 (Max, Default Fallback) supports text output.\n\nYes, Claude Fable 5.2 (Max, Default Fallback) supports image input and can analyze, describe, and answer questions about images.\n\nYes, Claude Fable 5.2 (Max, Default Fallback) is multimodal. It can process text and image input and generate text output.\n\nClaude Fable 5.2 (Max, Default Fallback) has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.\n\nNo, Claude Fable 5.2 (Max, Default Fallback) is proprietary. The model weights are not publicly available.\n\nClaude Fable 5.2 (Max, Default Fallback) is a proprietary model and Anthropic has not disclosed the model size or parameter count.\n\nClaude Fable 5.2 (Max, Default Fallback) achieves a score of 65 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.\n\nYes, Claude Fable 5.2 (Max, Default Fallback) is available via API through 1 provider. [Compare API providers](https://artificialanalysis.ai/models/claude-fable-5-2/providers)\n\nClaude Fable 5.2 (Max, Default Fallback) is available through 1 API provider. [Compare providers](https://artificialanalysis.ai/models/claude-fable-5-2/providers)", "url": "https://wpnews.pro/news/november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far", "canonical_source": "https://artificialanalysis.ai/models/claude-fable-5-2", "published_at": "2026-11-09 20:04:55+00:00", "updated_at": "2026-11-09 20:22:39.736708+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["Anthropic", "Claude Fable 5.2", "Artificial Analysis", "Artificial Analysis Intelligence Index"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far", "markdown": "https://wpnews.pro/news/november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far.md", "text": "https://wpnews.pro/news/november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far.txt", "jsonld": "https://wpnews.pro/news/november-2026-claude-fable-5-2-scores-65-on-artificial-analysis-highest-so-far.jsonld"}}