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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. Muse WonderMetaRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Muse Glimmer 30BMetaRemove
  4. Muse Spark OpenMetaRemove

4 is the maximum. Remove one to add another.

muse-wonder vs gemini-4-argon vs muse-glimmer-30b vs muse-spark-open
AttributeMuse Wondermuse-wonderGemini 4 Argongemini-4-argonMuse Glimmer 30Bmuse-glimmer-30bMuse Spark Openmuse-spark-open
Pricing
Input$4.00 / 1M$2.00 / 1M$0.35 / 1M$1.25 / 1M
Output$20.00 / 1M$10.00 / 1M$1.50 / 1M$4.25 / 1M
Cache Write (5m)$4.00 / 1M$2.00 / 1M$0.35 / 1M$1.25 / 1M
Cache Write (1h)$4.00 / 1M$2.00 / 1M$0.35 / 1M$1.25 / 1M
Cache Read$4.00 / 1M$2.00 / 1M$0.35 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M131K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderMetaGoogleMetaMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryMuse 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.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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.