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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. BGE Reranker V2 M3RerankerRemove
  2. Muse WonderMetaRemove
  3. Gemini 4 ArgonGoogleRemove
BAAI/bge-reranker-v2-m3 vs muse-wonder vs gemini-4-argon
AttributeBGE Reranker V2 M3BAAI/bge-reranker-v2-m3Muse Wondermuse-wonderGemini 4 Argongemini-4-argon
Pricing
Input$0.14 / 1M$4.00 / 1M$2.00 / 1M
Output$0.14 / 1M$20.00 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$4.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$2.00 / 1M
Cache ReadNot applicable$4.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context8.2K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderRerankerMetaGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
Summary—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.