Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
Workload (per month)
Anthropic: Claude Fable 5.1 (batch)
Sakana: Fugu Ultra
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$875
$975
Anthropic: Claude Fable 5.1 (batch) saves $100.00
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$850
$900
Anthropic: Claude Fable 5.1 (batch) saves $50.00
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$900
$960
Anthropic: Claude Fable 5.1 (batch) saves $60.00
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$550
$580
Anthropic: Claude Fable 5.1 (batch) saves $30.00
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$275
$280
Anthropic: Claude Fable 5.1 (batch) saves $5.00
Capabilities side by side
Capability
Anthropic: Claude Fable 5.1 (batch)
Sakana: Fugu Ultra
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
✓ Yes
spend extra tokens thinking before answering hard problems
Image input
✓ Yes
✓ Yes
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
✓ Yes
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
✓ Yes
✓ Yes
answer from live web results
Seed (reproducible sampling)
—
—
repeat a generation for tests and evals
Log-probabilities
—
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Price history
Anthropic: Claude Fable 5.1 (batch): price unchanged since we started tracking it on 2026-09-03.
Sakana: Fugu Ultra: price unchanged since we started tracking it on 2026-08-06.
Which should you choose?
Choose Anthropic: Claude Fable 5.1 (batch) if…
cost matters at scale — it is 11% cheaper on a typical 3:1 input/output mix ($875 vs $975 a month for 50,000 support chats)
you need file / PDF input — to accept documents without your own parsing step; Sakana: Fugu Ultra does not advertise it
Choose Sakana: Fugu Ultra if…
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Frequently asked questions
Is Anthropic: Claude Fable 5.1 (batch) better than Sakana: Fugu Ultra?
Anthropic: Claude Fable 5.1 (batch) comes out ahead on the data we track: 2 advantages and none for Sakana: Fugu Ultra.
Which is cheaper, Anthropic: Claude Fable 5.1 (batch) or Sakana: Fugu Ultra?
Anthropic: Claude Fable 5.1 (batch) is cheaper: $10.00 vs $11.25 per 1M tokens at a typical 3:1 input/output mix, 11% less. For 50,000 support conversations a month that is $875 against $975.
How much do Anthropic: Claude Fable 5.1 (batch) and Sakana: Fugu Ultra cost per million tokens?
Anthropic: Claude Fable 5.1 (batch) costs $5.00 per 1M input tokens and $25.00 per 1M output tokens; Sakana: Fugu Ultra costs $5.00 input and $30.00 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Anthropic: Claude Fable 5.1 (batch) or Sakana: Fugu Ultra?
Both take 1M tokens per request — roughly 1,500 pages of text.
Do Anthropic: Claude Fable 5.1 (batch) and Sakana: Fugu Ultra support function calling and JSON output?
Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual...
Fugu Ultra is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route...