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 Thinkingqwen3-next-80b-a3b-thinking | 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-Thinking is a reasoning-first chat model designed for difficult multi-step tasks such as math proofs, code synthesis and debugging, logical reasoning, and agentic planning. It outputs structured thinking traces by default, emphasizing stability over long chains of thought, efficient inference scaling, and strong instruction following. Suited for agent frameworks, tool use, retrieval-heavy workflows, and benchmarks requiring step-by-step solutions, it supports long detailed outputs and faster generation via throughput-optimized techniques, operating exclusively in thinking mode. | 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. |