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 | Claude Fable 5.2claude-fable-5.2 | Qwen3 Next 80B A3B Instructqwen3-next-80b-a3b-instruct |
|---|---|---|
| Pricing | ||
| Input | $8.00 / 1M | $0.10 / 1M |
| Output | $40.00 / 1M | $0.40 / 1M |
| Cache Write (5m) | $10.00 / 1M | $0.10 / 1M |
| Cache Write (1h) | $16.00 / 1M | $0.10 / 1M |
| Cache Read | $0.20 / 1M | $0.10 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 262.1K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Anthropic | Alibaba |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Claude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges. | 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. |