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 | mai-ds-r1:freeNot in the catalogue | GPT-6 Lunagpt-6-luna | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free |
|---|---|---|---|
| Pricing | |||
| Input | — | $0.10 / 1M | $0 / 1M |
| Output | — | $0.50 / 1M | $0 / 1M |
| Cache Write (5m) | — | $0.10 / 1M | — |
| Cache Write (1h) | — | $0.10 / 1M | — |
| Cache Read | — | $0.10 / 1M | $0 / 1M |
| Web Search | — | $0 / 1M | — |
| Cache Write | — | — | $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 | Free |
| Released | — | — | — |
| Description | |||
| Summary | — | GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability. | 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. |