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 | Qwen3 Next 80B A3B Instructqwen3-next-80b-a3b-instruct | Muse Wondermuse-wonder | GPT-6.1 Terragpt-6.1-terra |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | $4.00 / 1M | $1.00 / 1M |
| Output | $0.40 / 1M | $20.00 / 1M | $5.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | $4.00 / 1M | $1.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | $4.00 / 1M | $1.00 / 1M |
| Cache Read | $0.10 / 1M | $4.00 / 1M | $1.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 262.1K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | Alibaba | Meta | OpenAI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model optimized for fast, stable answers without exposing chain-of-thought. It handles complex reasoning, code generation, knowledge QA, and multilingual tasks with strong alignment and formatting reliability. Designed for high throughput and stability on ultra-long inputs and multi-turn conversations, it’s well suited for RAG, tool use, and agent workflows where consistent final outputs matter. It delivers near–large-model performance with efficient inference, making it a strong choice for production assistants and long-context problem solving. | 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. | 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. |