AI Models · Paid vs Paid

Claude Fable 5.1 (batch) Paid vs GPT-4o-mini Paid

Claude Fable 5.1 (batch) vs GPT-4o-mini compared — price per token, context window, multimodality, openness and which to choose. Full 2026 breakdown.

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

Claude Fable 5.1 (batch)PaidAnthropic
$5 /M input
$25 /M outputManaged API (no infra to run)
TypeProprietary
Context window1M tokens
MultimodalYes
Self-hostNo
GPT-4o-miniPaidOpenAI
$0.15 /M input
$0.6 /M outputManaged API (no infra to run)
TypeProprietary
Context window128K tokens
MultimodalYes
Self-hostNo
Choose GPT-4o-mini for the lower output price ($0.6/M vs $25/M). Choose Claude Fable 5.1 (batch) if you need the larger 1M context window.

Claude Fable 5.1 (batch) vs GPT-4o-mini specs

SpecClaude Fable 5.1 (batch)GPT-4o-miniWinner
MakerAnthropicOpenAI
TypeProprietaryProprietary
Context window1M tokens128K tokensClaude Fable 5.1 (batch)
Input price$5/M$0.15/MGPT-4o-mini
Output price$25/M$0.6/MGPT-4o-mini
Vision / multimodalYesYes= Tie
Tool / function callingYesYes= Tie
Self-hostableNo (API only)No (API only)
LicenseProprietaryProprietary

Price gap & when to choose each

42×cheaper per output token

GPT-4o-mini is ~42× cheaper than Claude Fable 5.1 (batch) on output tokens ($0.6 vs $25 per M tokens).

Choose Claude Fable 5.1 (batch) if…
  • You want frontier performance through a managed API
  • You need the longer 1M context window
  • You value reliability & ecosystem
  • You don't want to manage infrastructure
Choose GPT-4o-mini if…
  • You want frontier performance through a managed API
  • You want the lower output price
  • You value reliability & ecosystem
  • You don't want to manage infrastructure

Feature comparison

CapabilityClaude Fable 5.1 (batch)GPT-4o-mini
Open weights (downloadable)
Self-hostable
Runs fully offline
Vision / multimodal
Tool / function calling
1M+ context window

Benchmarks: Claude Fable 5.1 (batch) vs GPT-4o-mini

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

GPT-4o-mini delivers 9.5× more intelligence per dollar.
Claude Fable 5.1 (batch)
GPT-4o-mini
Intelligence index
53.4
6.7
Coding index
81.6
11.4
GPQA
93.7%
42.6%
Humanity's Last Exam
59.1%
4.2%
Long Context Reasoning
85.3%
SciCode
63.1%
τ-Bench Banking
47.2%
2.9%
Terminal-Bench
91.4%
5.6%
Math index
14.7
MMLU-Pro
64.8%
LiveCodeBench
23.4%
MATH-500
78.9%
AIME
11.7%
AIME 2025
14.7%
IFBench
31%
Speed
66.7 tok/s
0 tok/s
Latency
137.76s
0s
Intelligence per $
2.7
25.6

Benchmark data by Artificial Analysis.

How Claude Fable 5.1 (batch) and GPT-4o-mini score

🤝 Neck and neck on these criteria (3.1 vs 3.2 / 5).
CriterionClaude Fable 5.1 (batch)GPT-4o-mini
Cost-efficiency3.05.0
Context window5.03.5
Openness1.51.5
Self-hosting1.01.0
Multimodality5.05.0

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

Claude Fable 5.1 (batch) Paid

Anthropic · Proprietary

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...

GPT-4o-mini Paid

OpenAI · Proprietary

GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...

Frequently asked questions

Claude Fable 5.1 (batch) vs GPT-4o-mini — which is cheaper?

GPT-4o-mini is cheaper on output ($0.6/M vs $25/M).

Which has the larger context window?

Claude Fable 5.1 (batch) offers the larger context window (1M tokens).

Claude Fable 5.1 (batch) vs GPT-4o-mini — which should I pick in 2026?

Choose GPT-4o-mini for the lower output price ($0.6/M vs $25/M). Choose Claude Fable 5.1 (batch) if you need the larger 1M context window.

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