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.
| Attribute | GPT-6 Luna Progpt-6-luna-pro | mai-ds-r1:freeNot in the catalogue | Nemotron 3.5 Super VLnemotron-3.5-super-vl |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | — | $0.10 / 1M |
| Output | $0.50 / 1M | — | $0.40 / 1M |
| Cache Write (5m) | $0.10 / 1M | — | $0.10 / 1M |
| Cache Write (1h) | $0.10 / 1M | — | $0.10 / 1M |
| Cache Read | $0.10 / 1M | — | $0.10 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Context | |||
| Max context | 1.1M | — | 1M |
| Max output | N/A | — | N/A |
| Capabilities | |||
| Vision | Yes | — | Yes |
| Function Calling | Yes | — | Yes |
| JSON Mode | Yes | — | Yes |
| Streaming | Yes | — | Yes |
| Catalogue | |||
| Provider | OpenAI | — | NVIDIA |
| Category | chat | — | chat |
| Charge type | Pay As You Go | — | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | GPT-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. | — | 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. |