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.
- GPT-6.1 TerraOpenAIRemove
- Gemini 4 ArgonGoogleRemove
- Muse WonderMetaRemove
- Jina Embeddings V4Jina AIRemove
4 is the maximum. Remove one to add another.
| Attribute | GPT-6.1 Terragpt-6.1-terra | Gemini 4 Argongemini-4-argon | Muse Wondermuse-wonder | Jina Embeddings V4jina-embeddings-v4 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $1.00 / 1M | $2.00 / 1M | $4.00 / 1M | $0.05 / 1M |
| Output | $5.00 / 1M | $10.00 / 1M | $20.00 / 1M | — Not priced per output token |
| Cache Write (5m) | $1.00 / 1M | $2.00 / 1M | $4.00 / 1M | $0.05 / 1M |
| Cache Write (1h) | $1.00 / 1M | $2.00 / 1M | $4.00 / 1M | $0.05 / 1M |
| Cache Read | $1.00 / 1M | $2.00 / 1M | $4.00 / 1M | $0.05 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | — |
| Context | ||||
| Max context | 1M | 1M | 1M | 8.2K |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | Yes | Yes | No |
| Function Calling | Yes | Yes | Yes | No |
| JSON Mode | Yes | Yes | Yes | No |
| Streaming | No | Yes | Yes | No |
| Catalogue | ||||
| Provider | OpenAI | Meta | Jina AI | |
| Category | chat | chat | chat | embedding |
| Charge type | Pay As You Go | 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. | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. | 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. | — |