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 | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $1.00 / 1M | $0.125 / 1M | $4.00 / 1M |
| Output | $5.00 / 1M | — Not priced per output token | $20.00 / 1M |
| Cache Write (5m) | $1.00 / 1M | $0.125 / 1M | $4.00 / 1M |
| Cache Write (1h) | $1.00 / 1M | $0.125 / 1M | $4.00 / 1M |
| Cache Read | $1.00 / 1M | $0.125 / 1M | $4.00 / 1M |
| Web Search | $0 / 1M | — | $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 | Meta |
| Category | chat | embedding | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| 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. | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. |