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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. Qwen3.8 27BAlibabaRemove
  3. GPT-6 Luna ProOpenAIRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

muse-wonder vs qwen3.8-27b vs gpt-6-luna-pro vs deepseek-v4.1-flash
AttributeMuse Wondermuse-wonderQwen3.8 27Bqwen3.8-27bGPT-6 Luna Progpt-6-luna-proDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$4.00 / 1M$0.45 / 1M$0.10 / 1M$0.30 / 1M
Output$20.00 / 1M$3.20 / 1M$0.50 / 1M$1.20 / 1M
Cache Write (5m)$4.00 / 1M$0.45 / 1M$0.10 / 1M$0.30 / 1M
Cache Write (1h)$4.00 / 1M$0.45 / 1M$0.10 / 1M$0.30 / 1M
Cache Read$4.00 / 1M$0.45 / 1M$0.10 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K1.1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderMetaAlibabaOpenAIDeepSeek
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.Qwen3.8 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.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.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.