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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. Universal-3.5 ProAssemblyAIRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Muse WonderMetaRemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

universal-3-5-pro vs gemini-4-argon vs muse-wonder vs qwen3.8-27b
AttributeUniversal-3.5 Prouniversal-3-5-proGemini 4 Argongemini-4-argonMuse Wondermuse-wonderQwen3.8 27Bqwen3.8-27b
Pricing
Input— Not priced per input token$2.00 / 1M$4.00 / 1M$0.45 / 1M
Output— Not priced per output token$10.00 / 1M$20.00 / 1M$3.20 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$4.00 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$4.00 / 1M$0.45 / 1M
Cache ReadNot applicable$2.00 / 1M$4.00 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderAssemblyAIGoogleMetaAlibaba
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-22———
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. 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 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.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.