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

4 is the maximum. Remove one to add another.

qwen3.8-27b vs universal-3-5-pro vs muse-wonder vs gemini-4-argon
AttributeQwen3.8 27Bqwen3.8-27bUniversal-3.5 Prouniversal-3-5-proMuse Wondermuse-wonderGemini 4 Argongemini-4-argon
Pricing
Input$0.45 / 1M— Not priced per input token$4.00 / 1M$2.00 / 1M
Output$3.20 / 1M— Not priced per output token$20.00 / 1M$10.00 / 1M
Cache Write (5m)$0.45 / 1MNot applicable$4.00 / 1M$2.00 / 1M
Cache Write (1h)$0.45 / 1MNot applicable$4.00 / 1M$2.00 / 1M
Cache Read$0.45 / 1MNot applicable$4.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context262KN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderAlibabaAssemblyAIMetaGoogle
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-22——
Description
SummaryQwen3.8 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.AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.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.