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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. Gemini 3.6 FlashGoogleRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
muse-spark-open vs gemini-3.6-flash vs nemotron-3.5-super-vl:free
AttributeMuse Spark Openmuse-spark-openGemini 3.6 Flashgemini-3.6-flashNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$1.25 / 1M$1.50 / 1M$0 / 1M
Output$4.25 / 1M$7.50 / 1M$0 / 1M
Cache Write (5m)$1.25 / 1M$1.50 / 1M—
Cache Write (1h)$1.25 / 1M$1.50 / 1M—
Cache Read$1.25 / 1M$1.50 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaGoogleNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
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.Gemini 3.6 Flash is Google's high-efficiency model for coding, agentic workflows, and web and application development. It is optimized to produce polished, production-ready outputs with fewer unnecessary revisions, less hedging, and more direct task execution. By reducing both token usage and the number of model calls required to complete complex tasks, Gemini 3.6 Flash is well suited for high-throughput development, scalable agent systems, and cost-sensitive production 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.