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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. Hy4 previewTencentRemove
  3. GPT-6 Luna ProOpenAIRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs hy4-preview vs gpt-6-luna-pro vs muse-wonder
Attributemai-ds-r1:freeNot in the catalogueHy4 previewhy4-previewGPT-6 Luna Progpt-6-luna-proMuse Wondermuse-wonder
Pricing
Input—$0.834 / 1M$0.10 / 1M$4.00 / 1M
Output—$2.50 / 1M$0.50 / 1M$20.00 / 1M
Cache Write (5m)—$0.834 / 1M$0.10 / 1M$4.00 / 1M
Cache Write (1h)—$0.834 / 1M$0.10 / 1M$4.00 / 1M
Cache Read—$0.834 / 1M$0.10 / 1M$4.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M1.1M1M
Max output—N/AN/AN/A
Capabilities
Vision—YesYesYes
Function Calling—YesYesYes
JSON Mode—YesYesYes
Streaming—YesYesYes
Catalogue
Provider—TencentOpenAIMeta
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released————
Description
Summary—Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.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.