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
OpenAI: o1
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$1,750
$2,325
Anthropic: Claude Fable 5.1 saves $575
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$1,700
$2,400
Anthropic: Claude Fable 5.1 saves $700
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)
$1,800
$2,520
Anthropic: Claude Fable 5.1 saves $720
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$1,100
$1,560
Anthropic: Claude Fable 5.1 saves $460
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$550
$810
Anthropic: Claude Fable 5.1 saves $260
Capabilities side by side
Capability
Anthropic: Claude Fable 5.1
OpenAI: o1
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
✓ 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)
—
✓ Yes
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: price unchanged since we started tracking it on 2026-09-02.
OpenAI: o1: price unchanged since we started tracking it on 2026-03-10.
Which should you choose?
Choose Anthropic: Claude Fable 5.1 if…
cost matters at scale — it is 24% cheaper on a typical 3:1 input/output mix ($1,750 vs $2,325 a month for 50,000 support chats)
you need to fit long inputs in one request — 1M tokens is roughly 1,500 pages, against 300
you want the newer model — released 2026-09-01, 21 months after the other
Choose OpenAI: o1 if…
you need seed (reproducible sampling) — to repeat a generation for tests and evals; Anthropic: Claude Fable 5.1 does not advertise it
Frequently asked questions
Is Anthropic: Claude Fable 5.1 better than OpenAI: o1?
Neither wins outright: Anthropic: Claude Fable 5.1 is the pick when cost matters at scale, OpenAI: o1 when you need seed (reproducible sampling). In short: Anthropic: Claude Fable 5.1 when cost matters at scale, you need to fit long inputs in one request, you want the newer model; OpenAI: o1 when you need seed (reproducible sampling).
Which is cheaper, Anthropic: Claude Fable 5.1 or OpenAI: o1?
Anthropic: Claude Fable 5.1 is cheaper: $20.00 vs $26.25 per 1M tokens at a typical 3:1 input/output mix, 24% less. For 50,000 support conversations a month that is $1,750 against $2,325.
How much do Anthropic: Claude Fable 5.1 and OpenAI: o1 cost per million tokens?
Anthropic: Claude Fable 5.1 costs $10.00 per 1M input tokens and $50.00 per 1M output tokens; OpenAI: o1 costs $15.00 input and $60.00 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Anthropic: Claude Fable 5.1 or OpenAI: o1?
Anthropic: Claude Fable 5.1: 1M tokens, about 1,500 pages, against 200K (about 300 pages) for OpenAI: o1.
Do Anthropic: Claude Fable 5.1 and OpenAI: o1 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...
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...