AnthropicPixMind route verified · 2026-09-04Claude Fable 5 is Anthropic's highest-quality route in this PixMind set for long-running agent work, difficult codebases, and tasks that must preserve state across many steps.
maximum-quality long-horizon agents, difficult coding, and memory-heavy work
Selected model
Claude Fable 5
Claude Fable 5 is Anthropic's highest-quality route in this PixMind set for long-running agent work, difficult codebases, and tasks that must preserve state across many steps.
Three jobs to try first
Decompose a long-running goal into checkpoints, tools, verification, and recovery steps.
Prompt starter
Plan and execute [goal] in checkpoints. Before each action state the evidence needed; after each action verify the result and update the remaining plan. Stop on irreversible ambiguity.
Trace behavior across files, identify failure paths, and propose a scoped patch plan.
Prompt starter
Review the attached code for [goal]. Trace the request end to end, list findings by severity with file evidence, then propose the smallest safe patch and focused tests.
Extract decisions, disagreements, evidence, and action items from a large source set.
Prompt starter
Analyze these documents as one evidence set. Separate confirmed facts, open questions, and contradictions. Produce an executive summary plus a source-linked action table.
This PixMind route accepts text and images. Video, audio, or PDF availability is shown separately and should not be inferred from the model family.
Adaptive thinking; effort low to max
Input
7,644
points per 1M tokens
Output
38,220
points per 1M tokens
Compare the current route with nearby models before spending tokens on a long conversation.
| Model | Best for | PixMind inputs | Max output | Input points / 1M |
|---|---|---|---|---|
| Claude Fable 5Current page | maximum-quality long-horizon agents, difficult coding, and memory-heavy work | Text · Vision | 131,072 | 7,644 |
| Claude Opus 5 | complex professional reasoning, autonomous coding, and self-correcting workflows | Text · Vision | 131,072 | 3,822 |
| Claude Sonnet 5 | production coding, tool use, and structured knowledge work with balanced cost | Text · Vision | 131,072 | 1,528.8 |
| GPT-5.6 Sol | demanding reasoning, vision, and tool-driven work | Text · Vision | 128,000 | 3,694.6 |
complex professional reasoning, autonomous coding, and self-correcting workflows
Start chattingAnthropicproduction coding, tool use, and structured knowledge work with balanced cost
Start chattingOpenAIdemanding reasoning, vision, and tool-driven work
Start chattingModel-family claims come from provider documentation. Inputs, limits, and points are the PixMind route snapshot verified on September 4, 2026.
Decompose a long-running goal into checkpoints, tools, verification, and recovery steps. Start with this template: Plan and execute [goal] in checkpoints. Before each action state the evidence needed; after each action verify the result and update the remaining plan. Stop on irreversible ambiguity.
Trace behavior across files, identify failure paths, and propose a scoped patch plan. Start with this template: Review the attached code for [goal]. Trace the request end to end, list findings by severity with file evidence, then propose the smallest safe patch and focused tests.
Extract decisions, disagreements, evidence, and action items from a large source set. Start with this template: Analyze these documents as one evidence set. Separate confirmed facts, open questions, and contradictions. Produce an executive summary plus a source-linked action table.
This PixMind route accepts text and images. Video, audio, or PDF availability is shown separately and should not be inferred from the model family. The current catalog flags: text, vision.
State the outcome and constraints directly. For long work, define checkpoints and the expected deliverable instead of repeatedly asking the model to be thorough.
Current directory pricing is 7644 input points and 38220 output points per 1 million tokens.
Claude Fable 5 is positioned for maximum-quality long-horizon agents, difficult coding, and memory-heavy work. Check the comparison table when lower cost, different input support, or a heavier reasoning tier matters more.
A network interruption, insufficient balance, or temporary model availability can stop a reply. Before resending, check whether token usage and points were already settled.
Use the prompt box above to send a focused first task, then continue in the full AI Chat workspace.
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