Claude Fable 5

Name: Claude Fable 5 — independent benchmark scores
Creator: Epoch AI
License: https://creativecommons.org/licenses/by/4.0/

Vertex Ai Anthropic ModelsChat1M context· released 2026-06-09Floating alias

vertex_ai/claude-fable-5

Estimate cost Compare

Prices as of 2026-07-14 · rev 179affb· unknown is shown as unknown, never $0.00

Strongest atReasoning · Mathematics · Code & agentic · Factualitytop-quartile among comparable models scored on these domains

Data confidence4/4 exactbenchmarks measured on this exact model · prices 2026-07-14

Cost / request$0.0351K in · 500 out

Input / M$10.00

Output / M$50.00

Cache read / M$1.00

Context1M1,000,000 tokens

Max output128K128,000 tokens

Capability fingerprint

independent benchmarks, grouped by domain · higher is better · not our measurement

Reasoning

SimpleBencheveryday reasoning (unsaturated)

78%99th pctile

exact modelSimpleBench Leaderboard · Claude Fable 5

Mathematics

AIME (OTIS mock)olympiad math

100%98th pctile

exact modeloptimized runEpoch evaluations · Claude Fable 5

Code & agentic

WeirdMLnovel out-of-distribution ML coding

88%98th pctile

exact modelWeirdML Leaderboard · Claude Fable 5

Factuality

SimpleQAfactual accuracy (higher = fewer hallucinations)

68%90th pctile

exact modelEpoch evaluations · Claude Fable 5

peer median · percentile ranks this model among distinct comparable models (deduped — one entry per model, not per provider) scored on that benchmark · exact model / family proxy per row.

Source: Epoch AI — “AI Benchmarking Hub” (epoch.ai), CC-BY (Epoch AI). Some rows carry an upstream leaderboard (shown per row); Aider & Terminal-Bench are additionally Apache-2.0. Each benchmark measures a different thing on a different scale — rows are not comparable across domains, and a score is not a guarantee of quality on your task.

Capabilities Index

Epoch AI's cross-model capability re-fit · one comparable axis · not our measurement

160.7ECI

98th percentile of 150 modelsexact model

The dot is Claude Fable 5; the whisker is its 95% CI; the firm tick is the peer median; faint ticks are every other scored model. Unlike a raw benchmark, the ECI axis is comparable across models — but it's a relative re-fit scale, so a model's exact position can shift when Epoch recomputes it.

Model facts · Epoch AI, confident confidence

Developer: Anthropic
First published: 2026-06-09
Access: API access

Source: Epoch AI — Capabilities Index & Notable-Models data (epoch.ai), CC-BY (Epoch AI). The ECI is Epoch's index, not TokenTriage's; per-fact confidence labels are Epoch's own.

Human preference (LMArena)

blind pairwise human votes · Elo · style-controlled · not a correctness score

exact modelmeasured on claude-fable-5

Each dot is Claude Fable 5's Arena score in that category; the whisker is the 95% CI; the firm tick is the peer median across distinct models; faint ticks are the field. This is human preference — which answer people pick in a blind A/B — not a correctness or capability score, and adjacent ranks routinely overlap within their intervals.

Source: LMArena (LMArena Leaderboard (lmarena.ai) — lmarena-ai/leaderboard-dataset on Hugging Face, CC BY 4.0.). Ratings are the style-controlled estimates (length/formatting confound removed), as of 2026-07-27.

Also worth considering

cheaper models that score about as well — pick the benchmark that matters to you

1 cheaper model scores within 3 points of Claude Fable 5 on SimpleBench.

Model	SimpleBench	Cost / req	Savings
GGGemini 3.1 Pro Preview	76%-3	$0.008	−77%

Deduped to one entry per model (cheapest offering), priced at the reference 1K-in / 500-out request. Filtered only on the benchmark score — a model with an unknown capability is never silently excluded. A lower price is only cheaper if quality holds on your task — that's what routing proves.

Pricing

Input · fresh prompt tokens	$10.00 / M
Output · generated tokens	$50.00 / M
Cache read · cached-input hit	$1.00 / M
Cache write · 5-minute TTL	$12.50 / M
Cache write · 1-hour TTL	$20.00 / M
Reasoning · thinking tokens	unknown

Reasoning tokens: no separate rate is published. Providers typically bill thinking tokens at the output rate ($50.00 / M) — which is what the estimator assumes, so an extended-thinking workload isn't silently undercounted.

Model info

Provider: Vertex Ai Anthropic Models
Catalog key: vertex_ai-anthropic_models/vertex_ai/claude-fable-5
Identifier: Floating alias (may re-point over time)
Type: Chat
Max input: 1,000,000 tokens
Max output: 128,000 tokens
Released: 2026-06-09
Knowledge cutoff: 2026-01-31
Weights: Proprietary
Price snapshot: 2026-07-14 · rev 179affb

Pricing from TokenTriage's resolved price artifact (TokenTriage resolved price artifact (internal/pricing/data/prices.json)); capability + model metadata is third-party (LiteLLM model_prices_and_context_window.json, models.dev). Unknown means unstated, not absent.

Prompt-cache break-even

reuse the same prefix ≥ 2 times and caching wins

Writing a prefix to cache costs a premium, but each subsequent hit reads at $1.00/M instead of $10.00/M. For this model the crossover is 2 reuses — past that, the same prompt keeps getting cheaper. TokenTriage's semantic cache is built to cross that line for you.

Capabilities

7 documented as supported

Input & output

✓Vision

✓PDF input

?Audio input

?Audio output

API behaviour

?Streaming

✓Structured output

✓Prompt caching

✓Reasoning

Agents & tools

✓Function calling

?Parallel tool calls

?Web search

✓Computer use

✓ supported ·✕ not supported ·? not documented ·⚠ sources disagree. A models.dev tag means the LiteLLM catalog was silent and an independent source supplied the value (lower confidence); ? is never silently turned into a confident ✕.

Estimate a request

disjoint token buckets · input = fresh (uncached) tokens

Preset

Input tokens

Output tokens

Cached input

Cache write

Reasoning

Requests / month

Cost / request—

Projected / month—

How output length drives the bill

at a 1K-token prompt · cost scales linearly with output

Output tokens overtake input spend at ~200 tokens for this model — past that, generation is your bill, whatever the headline input price says. Computed by the same cost engine as the estimate above.

Compare Claude Fable 5 with other models →

Route to Claude Fable 5

OpenAI-compatible · one base URL

curl https://YOUR-TOKENTRIAGE-HOST/v1/chat/completions \
  -H "Authorization: Bearer $TOKENTRIAGE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vertex_ai-anthropic_models/vertex_ai/claude-fable-5",
    "messages": [{"role": "user", "content": "Hello"}]
  }'from openai import OpenAI

client = OpenAI(
    base_url="https://YOUR-TOKENTRIAGE-HOST/v1",
    api_key="YOUR_TOKENTRIAGE_KEY",
)

resp = client.chat.completions.create(
    model="vertex_ai-anthropic_models/vertex_ai/claude-fable-5",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Point your existing OpenAI client at your TokenTriage host and pass the catalog key as the model. TokenTriage attributes the real cost per request and can route to a cheaper model once it's proven just as good on your traffic.

Data & API

machine-readable · rev 179affb

Pull this model's pricing, capabilities, benchmarks and cross-source provenance programmatically — every field carries the snapshot rev, and unknown is null, never 0. Free to reuse with attribution: benchmarks & Capabilities Index © Epoch AI (CC-BY (Epoch AI)); human-preference Elo © LMArena (CC-BY-4.0); capability & price cross-check via models.dev v2 (MIT) · Portkey (MIT) · TrueFoundry (MIT); open-weights facts via Hugging Face (per-repo license).

vertex-ai-anthropic-models-vertex-ai-claude-fable-5.json vertex-ai-anthropic-models-vertex-ai-claude-fable-5.csv

A price is a guess until it meets your traffic.

This page tells you what Claude Fable 5 charges per token. TokenTriage tells you what it costs on your real requests — and cuts the bill only after proving a cheaper model is just as good.

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