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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. Voxtral Small 24B 2507Mistral AIRemove
  2. Claude Fable 5.2AnthropicRemove
  3. Gemini 4 ArgonGoogleRemove
voxtral-small-24b-2507 vs claude-fable-5.2 vs gemini-4-argon
AttributeVoxtral Small 24B 2507voxtral-small-24b-2507Claude Fable 5.2claude-fable-5.2Gemini 4 Argongemini-4-argon
Pricing
Input$0.125 / 1M$8.00 / 1M$2.00 / 1M
Output$0.375 / 1M$40.00 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$10.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$16.00 / 1M$2.00 / 1M
Cache ReadNot applicable$0.20 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context32K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingYesYesYes
Catalogue
ProviderMistral AIAnthropicGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryVoxtral Small is an upgraded version of Mistral Small 3 that adds advanced audio understanding while preserving strong text performance. It handles speech transcription, translation, and audio comprehension, with audio input billed per million seconds.Claude 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.