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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. MiniMax M2MiniMaxRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. Muse Spark OpenMetaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

minimax-m2 vs nemotron-3.5-super-vl vs muse-spark-open vs muse-wonder
AttributeMiniMax M2minimax-m2Nemotron 3.5 Super VLnemotron-3.5-super-vlMuse Spark Openmuse-spark-openMuse Wondermuse-wonder
Pricing
Input$0.15 / 1M$0.10 / 1M$1.25 / 1M$4.00 / 1M
Output$0.45 / 1M$0.40 / 1M$4.25 / 1M$20.00 / 1M
Cache Write (5m)$0.15 / 1M$0.10 / 1M$1.25 / 1M$4.00 / 1M
Cache Write (1h)$0.15 / 1M$0.10 / 1M$1.25 / 1M$4.00 / 1M
Cache Read$0.15 / 1M$0.10 / 1M$1.25 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context196.6K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderMiniMaxNVIDIAMetaMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryMiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.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.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.