Fable 5 vs Sonnet 5 — token cost (Njenga)
A member-only Medium post by Joe Njenga (July 2026) comparing what Claude Fable 5 and Claude Sonnet 5 cost to run inside Claude Code, framed as a lesson in “how to stop burning tokens.” Only the baseline test is readable without a Medium subscription; the coding comparison that follows is behind the paywall.
The baseline anecdote
Njenga typed a single "Hello" into Claude Code on each model and read off the cost stats the CLI
reports. His numbers for that one prompt:
| Fable 5 | Sonnet 5 | |
|---|---|---|
| input tokens | 13.1k | 8.7k |
| cache read | 15.6k | 24.8k |
| output tokens | 80 | 141 |
| wall-clock | 7 s | 3 s |
| cost | $0.4745 | cheaper (~3.2× less) |
His read: Sonnet 5 pulled more from cache (24.8k vs 15.6k), and since cache reads are billed below fresh input, the model that caches better ends up cheaper — even while generating more output. He calls the gap a “3.2× pricing difference” on a single-word prompt and says it widened his curiosity about how the two scale on real coding tasks (that section is paywalled).
Why “Hello” costs 47 cents
The eye-catching $0.4745 is not the price of one word. In Claude Code every turn ships the whole
system prompt, tool definitions, and accumulated session context, so a trivial "Hello" still
carries ~13–25k tokens of input + cache. The post’s own token counts make that plain; the headline
is the harness overhead, not the greeting.
How much to trust this
T4, and treat the numbers as one anecdote. It’s a paywalled personal blog, a single run per model, no repetition or averaging, and the striking claim — that Fable 5, the Claude 5-family model built for fast Claude Code work, is ~3× more expensive than Sonnet 5 on the same prompt — runs against the intuition that a lightweight model should be the cheap option. It may be real (caching behaviour and per-token rates differ by model), but nothing here is authoritative pricing. The open question is flagged in synthesis; it wants a T1 Anthropic price sheet to settle. See llm-api-pricing for the caching lever the post leans on.
Cross-spoke context
The setting is Claude Code (an agentic coding harness → agentic-tooling-wiki) and the mechanism is prompt caching (an inference-serving detail → llm-inference-wiki, kv-cache). The in-scope substance here is the model cost comparison, so it lives in this spoke; those two facets are noted, not re-paged.