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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. Transcribe 1 ProFish AudioRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. Qwen3.8 27BAlibabaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs gemini-3.8-flash vs qwen3.8-27b vs muse-wonder
AttributeTranscribe 1 Protranscribe-1-proGemini 3.8 Flashgemini-3.8-flashQwen3.8 27Bqwen3.8-27bMuse Wondermuse-wonder
Pricing
Input— Not priced per input token$0.75 / 1M$0.45 / 1M$4.00 / 1M
Output— Not priced per output token$3.75 / 1M$3.20 / 1M$20.00 / 1M
Cache Write (5m)Not applicable$0.75 / 1M$0.45 / 1M$4.00 / 1M
Cache Write (1h)Not applicable$0.75 / 1M$0.45 / 1M$4.00 / 1M
Cache ReadNot applicable$0.75 / 1M$0.45 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M262K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderFish AudioGoogleAlibabaMeta
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-24———
Description
SummaryFish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.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.