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.

Qwen3.8 27BAlibaba Remove
Muse WonderMeta Remove
Gemini 4 ArgonGoogle Remove
Gemini 3.5 TranscribeGoogle Remove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs muse-wonder vs gemini-4-argon vs gemini-3.5-transcribe
Attribute	Qwen3.8 27Bqwen3.8-27b	Muse Wondermuse-wonder	Gemini 4 Argongemini-4-argon	Gemini 3.5 Transcribegemini-3.5-transcribe
Pricing
Input	$0.45 / 1M	$4.00 / 1M	$2.00 / 1M	— Not priced per input token
Output	$3.20 / 1M	$20.00 / 1M	$10.00 / 1M	— Not priced per output token
Cache Write (5m)	$0.45 / 1M	$4.00 / 1M	$2.00 / 1M	Not applicable
Cache Write (1h)	$0.45 / 1M	$4.00 / 1M	$2.00 / 1M	Not applicable
Cache Read	$0.45 / 1M	$4.00 / 1M	$2.00 / 1M	Not applicable
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	262K	1M	1M	98.3K
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes	Yes
Function Calling	Yes	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes	No
Streaming	Yes	Yes	Yes	No
Catalogue
Provider	Alibaba	Meta	Google	Google
Category	chat	chat	chat	voice
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—	2026-09-25
Description
Summary	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.	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.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.