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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. Muse Spark OpenMetaRemove
  4. Perceptron Mk1.5PerceptronRemove

4 is the maximum. Remove one to add another.

muse-wonder vs qwen3.8-27b vs muse-spark-open vs perceptron-mk1.5
AttributeMuse Wondermuse-wonderQwen3.8 27Bqwen3.8-27bMuse Spark Openmuse-spark-openPerceptron Mk1.5perceptron-mk1.5
Pricing
Input$4.00 / 1M$0.45 / 1M$1.25 / 1M$0.15 / 1M
Output$20.00 / 1M$3.20 / 1M$4.25 / 1M$1.50 / 1M
Cache Write (5m)$4.00 / 1M$0.45 / 1M$1.25 / 1M$0.15 / 1M
Cache Write (1h)$4.00 / 1M$0.45 / 1M$1.25 / 1M$0.15 / 1M
Cache Read$4.00 / 1M$0.45 / 1M$1.25 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K1M36.9K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesYes
Catalogue
ProviderMetaAlibabaMetaPerceptron
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released———2026-09-25
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.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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.