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.

Gemini 4 ArgonGoogle Remove
Transcribe 1 ProFish Audio Remove
Qwen3.8 27BAlibaba Remove
Muse WonderMeta Remove

4 is the maximum. Remove one to add another.

gemini-4-argon vs transcribe-1-pro vs qwen3.8-27b vs muse-wonder
Attribute	Gemini 4 Argongemini-4-argon	Transcribe 1 Protranscribe-1-pro	Qwen3.8 27Bqwen3.8-27b	Muse Wondermuse-wonder
Pricing
Input	$2.00 / 1M	— Not priced per input token	$0.45 / 1M	$4.00 / 1M
Output	$10.00 / 1M	— Not priced per output token	$3.20 / 1M	$20.00 / 1M
Cache Write (5m)	$2.00 / 1M	Not applicable	$0.45 / 1M	$4.00 / 1M
Cache Write (1h)	$2.00 / 1M	Not applicable	$0.45 / 1M	$4.00 / 1M
Cache Read	$2.00 / 1M	Not applicable	$0.45 / 1M	$4.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	N/A	262K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes	Yes
Function Calling	Yes	No	Yes	Yes
JSON Mode	Yes	No	Yes	Yes
Streaming	Yes	No	Yes	Yes
Catalogue
Provider	Google	Fish Audio	Alibaba	Meta
Category	chat	voice	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-24	—	—
Description
Summary	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.	Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <\|speaker:0\|>. Billed per second of audio.	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.