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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Claude Fable 5.2AnthropicRemove
  2. Gemini 4 ArgonGoogleRemove
  3. o4 Mini Deep ResearchOpenAIRemove
claude-fable-5.2 vs gemini-4-argon vs o4-mini-deep-research
AttributeClaude Fable 5.2claude-fable-5.2Gemini 4 Argongemini-4-argono4 Mini Deep Researcho4-mini-deep-research
Pricing
Input$8.00 / 1M$2.00 / 1M$2.00 / 1M
Output$40.00 / 1M$10.00 / 1M$8.00 / 1M
Cache Write (5m)$10.00 / 1M$2.00 / 1M$2.00 / 1M
Cache Write (1h)$16.00 / 1M$2.00 / 1M$2.00 / 1M
Cache Read$0.20 / 1M$2.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M200K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAnthropicGoogleOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryClaude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.o4-mini-deep-research is a faster, lower-cost version of OpenAI's deep-research model, designed for complex, multi-step investigations. It automatically relies on web_search for information gathering, which always adds extra usage cost.