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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. GPT-6 Luna ProOpenAIRemove
  2. Muse Spark OpenMetaRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
gpt-6-luna-pro vs muse-spark-open vs nemotron-3.5-super-vl:free
AttributeGPT-6 Luna Progpt-6-luna-proMuse Spark Openmuse-spark-openNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$0.10 / 1M$1.25 / 1M$0 / 1M
Output$0.50 / 1M$4.25 / 1M$0 / 1M
Cache Write (5m)$0.10 / 1M$1.25 / 1M—
Cache Write (1h)$0.10 / 1M$1.25 / 1M—
Cache Read$0.10 / 1M$1.25 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1.1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIMetaNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryGPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.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.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.