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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. Qwen Audio 3.0 TTS FlashQwenRemove
  3. Gemini 4 ArgonGoogleRemove
claude-fable-5.2 vs qwen-audio-3.0-tts-flash vs gemini-4-argon
AttributeClaude Fable 5.2claude-fable-5.2Qwen Audio 3.0 TTS Flashqwen-audio-3.0-tts-flashGemini 4 Argongemini-4-argon
Pricing
Input$8.00 / 1M— Not priced per input token$2.00 / 1M
Output$40.00 / 1M— Not priced per output token$10.00 / 1M
Cache Write (5m)$10.00 / 1MNot applicable$2.00 / 1M
Cache Write (1h)$16.00 / 1MNot applicable$2.00 / 1M
Cache Read$0.20 / 1MNot applicable$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderAnthropicQwenGoogle
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-07-23—
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.Qwen Audio 3.0 TTS Flash text-to-speech. Billed per input character; each Chinese (Han) character counts as 2.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.