Anthropic · Writing, knowledge and maximum quality
Claude Fable 5
A very strong general-purpose model for demanding writing, complex reasoning and working with facts, when higher costs are not the main constraint.
- Costs
- Very high · use only where the benefit justifies the cost
- Speed
- Slow at the most thorough setting
- Availability
- Online service and apps from Anthropic
- Input length
- Handles long inputs; the exact limit depends on the service used
- Inputs
- Text and image inputs
Data checked . Release date unverified.
Indicative capability profile
Where the model is strong
The five levels are our clear summary of the results below. They are not a ranking that applies to every task.
Ideal use
When to choose it
- Long specialist texts and editorial content
- Tasks combining knowledge, logical reasoning and precise instructions
- Second review of strategic materials
Usage boundaries
When to choose another model
- Bulk routine processing
- Tasks where cost per request is the main criterion
Traceable supporting sources
Data and measurement sources
Distinguish between the manufacturer's documentation and the results of a specific test. Measurements also depend on the settings and the task set used.
LiveBench 2026-06-25
80.8 overall · 90.7 for languageVery good result in language, problem-solving and coding.
Open original source ↗Intelligence Index v4.1
60 pointsPerformance among the best models; the newer Opus 5 reached a similar level at a lower cost.
Open original source ↗Trends over time
Related events from AI Radar
Anthropic challenges US approach in the Claude Fable 5 and Mythos 5 export control case, responds with jailbreak severity framework
New: Export control issued due to concerns about a jailbreak and the model's cyber capabilities; Anthropic challenged the legality of the export control and the lack of a clear process; Anthropic responded by publishing a jailbreak severity framework
Security researchers and developers now have access to a formal Anthropic program and framework for reporting and assessing the severity of jailbreaks affecting Claude models.
Companies dependent on the Claude Fable 5 or Mythos 5 models face the risk that US export controls could temporarily restrict their access to the model without a clearly defined process, which is relevant for supply chain and geopolitical risk planning.
Claude Opus 5.5 available on Amazon Bedrock with lower costs per task
According to Anthropic, developers working with long-running agentic tasks or extensive documents can expect clearer ongoing updates on model progress and less need to correct outputs, but also more frequent refusals of requests due to stricter safety classifiers.
According to Anthropic, companies running agentic tasks on Amazon Bedrock may achieve lower average costs per task thanks to lower token prices and significantly cheaper cache reads, which is particularly relevant for long-running and repeated workloads.
Anthropic has released Claude Opus 5 – a cheaper model close to Fable 5 in performance
Claude Pro and Max users get Opus 5 as the default or most capable available model with no price change; the choice of "effort" level (low to max) directly affects the balance of speed, quality, and cost for individual queries.
Companies deploying Claude in products or development gain a model that, according to Anthropic, comes close to Fable 5 in knowledge work and agentic coding at half the token price, changing the cost calculation for running AI.
OpenAI halved prices for the GPT-6 Sol and Luna models; independent analysis does not confirm a performance improvement
New: Anthropic released Claude Opus 5.5 on the same day (2026-09-23), roughly an hour after OpenAI; Claude Opus 5.5 is 20% cheaper than the previous Opus 5.0 ($4/$20 vs $5/$25); Opus 5.5 improved communication style and token efficiency; The model releases launch a 'price war' between the two largest AI companies; GPT-6 Luna and Sol are part of a broader strategy of price competition, not an isolated release
Developers working with the API from OpenAI can reduce inference costs by up to half while maintaining the same usage pattern (Sol for more complex tasks, Luna for high-volume simple tasks), but independent tests suggest that a lower price may not mean higher or even the same output quality in all cases.
Businesses gain a cheaper option for reasoning models for high-volume or repetitive tasks (summaries, data extraction, code), but according to an independent analysis, actual output quality has not improved compared with the previous generation and has instead declined on some knowledge work tasks, so deployment decisions should not rely solely on the manufacturer's claims about price/performance.
Next step
Compare the model using your actual task.
A benchmark narrows the selection. A short trial on your data determines the choice.