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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 Spark OpenMetaRemove
  2. Magistral Medium 2506 (Thinking)Mistral AIRemove
  3. Muse WonderMetaRemove
muse-spark-open vs magistral-medium-2506:thinking vs muse-wonder
AttributeMuse Spark Openmuse-spark-openMagistral Medium 2506 (Thinking)magistral-medium-2506:thinkingMuse Wondermuse-wonder
Pricing
Input$1.25 / 1M$2.00 / 1M$4.00 / 1M
Output$4.25 / 1M$5.00 / 1M$20.00 / 1M
Cache Write (5m)$1.25 / 1M$2.00 / 1M$4.00 / 1M
Cache Write (1h)$1.25 / 1M$2.00 / 1M$4.00 / 1M
Cache Read$1.25 / 1M$2.00 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M41.0K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderMetaMistral AIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryMuse 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.—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.