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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. GLM 4.7 FlashZ.AIRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
muse-spark-open vs glm-4.7-flash vs nemotron-3.5-super-vl
AttributeMuse Spark Openmuse-spark-openGLM 4.7 Flashglm-4.7-flashNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$1.25 / 1M$0.06 / 1M$0.10 / 1M
Output$4.25 / 1M$0.40 / 1M$0.40 / 1M
Cache Write (5m)$1.25 / 1M$0.06 / 1M$0.10 / 1M
Cache Write (1h)$1.25 / 1M$0.06 / 1M$0.10 / 1M
Cache Read$1.25 / 1M$0.06 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M200K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaZ.AINVIDIA
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.GLM-4.7-Flash is a state-of-the-art 30B-class model designed to strike a strong balance between performance and efficiency. It is specifically optimized for agentic coding scenarios, with enhanced capabilities in code generation, long-horizon task planning, and tool-based collaboration. Among open-source models of comparable size, GLM-4.7-Flash has achieved leading results on multiple public benchmark leaderboards, establishing itself as a competitive and practical choice for advanced developer and agent workflows.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.