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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. mai-ds-r1:freeNot in the catalogueRemove
  2. Muse Spark 1.3MetaRemove
  3. Muse WonderMetaRemove
  4. GPT-6 LunaOpenAIRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs muse-spark-1.3 vs muse-wonder vs gpt-6-luna
Attributemai-ds-r1:freeNot in the catalogueMuse Spark 1.3muse-spark-1.3Muse Wondermuse-wonderGPT-6 Lunagpt-6-luna
Pricing
Input—$1.25 / 1M$4.00 / 1M$0.10 / 1M
Output—$4.25 / 1M$20.00 / 1M$0.50 / 1M
Cache Write (5m)—$1.25 / 1M$4.00 / 1M$0.10 / 1M
Cache Write (1h)—$1.25 / 1M$4.00 / 1M$0.10 / 1M
Cache Read—$1.25 / 1M$4.00 / 1M$0.10 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M1M1.1M
Max output—N/AN/AN/A
Capabilities
Vision—YesYesYes
Function Calling—YesYesYes
JSON Mode—YesYesYes
Streaming—YesYesYes
Catalogue
Provider—MetaMetaOpenAI
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released————
Description
Summary—Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.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.GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability.