Skip to content

Compare models

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

  1. mai-ds-r1:freeNot in the catalogueRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. Transcribe 1 ProFish AudioRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs gemini-3.8-flash vs transcribe-1-pro vs muse-wonder
Attributemai-ds-r1:freeNot in the catalogueGemini 3.8 Flashgemini-3.8-flashTranscribe 1 Protranscribe-1-proMuse Wondermuse-wonder
Pricing
Input—$0.75 / 1M— Not priced per input token$4.00 / 1M
Output—$3.75 / 1M— Not priced per output token$20.00 / 1M
Cache Write (5m)—$0.75 / 1MNot applicable$4.00 / 1M
Cache Write (1h)—$0.75 / 1MNot applicable$4.00 / 1M
Cache Read—$0.75 / 1MNot applicable$4.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1MN/A1M
Max output—N/AN/AN/A
Capabilities
Vision—YesNoYes
Function Calling—YesNoYes
JSON Mode—YesNoYes
Streaming—YesNoYes
Catalogue
Provider—GoogleFish AudioMeta
Category—chatvoicechat
Charge type—Pay As You GoPay As You GoPay As You Go
Released——2026-09-24—
Description
Summary—Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.