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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 4 ArgonGoogleRemove
  3. Muse Glimmer 30BMetaRemove
transcribe-1-pro vs gemini-4-argon vs muse-glimmer-30b
AttributeTranscribe 1 Protranscribe-1-proGemini 4 Argongemini-4-argonMuse Glimmer 30Bmuse-glimmer-30b
Pricing
Input— Not priced per input token$2.00 / 1M$0.35 / 1M
Output— Not priced per output token$10.00 / 1M$1.50 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$0.35 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.35 / 1M
Cache ReadNot applicable$2.00 / 1M$0.35 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M131K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderFish AudioGoogleMeta
Categoryvoicechatchat
Charge typePay 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 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.Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It combines strong multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across 100+ languages. Designed for long-horizon agentic and coding workflows, Muse Glimmer 30B offers a practical balance of capability and deployment efficiency, making it well suited for local coding assistants, multimodal agents, and production workflows that require sustained autonomous execution.