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-6.1 Terragpt-6.1-terra | Mistral Embed 2312mistral-embed-2312 | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free |
|---|---|---|---|
| Pricing | |||
| Input | $1.00 / 1M | $0.125 / 1M | $0 / 1M |
| Output | $5.00 / 1M | — Not priced per output token | $0 / 1M |
| Cache Write (5m) | $1.00 / 1M | $0.125 / 1M | — |
| Cache Write (1h) | $1.00 / 1M | $0.125 / 1M | — |
| Cache Read | $1.00 / 1M | $0.125 / 1M | $0 / 1M |
| Web Search | $0 / 1M | — | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | 1M | 8.2K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | No | No | Yes |
| Catalogue | |||
| Provider | OpenAI | Mistral AI | NVIDIA |
| Category | chat | embedding | chat |
| Charge type | Pay As You Go | Pay As You Go | Free |
| Released | — | — | — |
| Description | |||
| Summary | GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost. | Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text. | 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. |