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 | Nemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free | GPT-6 Sol Progpt-6-sol-pro |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | $0 / 1M | $2.00 / 1M |
| Output | $0.50 / 1M | $0 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | — | $2.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | — | $2.00 / 1M |
| Cache Read | $0.10 / 1M | $0 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Cache Write | — | $0 / 1M | — |
| Context | |||
| Max context | 1.1M | 1M | 1.1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | NVIDIA | OpenAI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Free | 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 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key. | GPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost. |