Agent runs spit out a lot of tokens and burn money when tool loops go wrong. Before you wire one up, check tool calling, JSON reliability, context length, latency, and output price. Then set a budget so a bad loop cannot empty the account overnight.
來源依據: NextModel capability mapping and supported-parameter metadata when available. · 更新日期 2026-07-01
如何使用这份名单
匹配分
先從候選名單開始，再以真實提示詞測試，並在接入正式環境路由前比較月度成本。
Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...
Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...
Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...
Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...
比較表
當你在縮小正式環境候選名單、建立備援策略或比較模型經濟性時，可使用此視圖。
| Model | Provider | Input | Output | Context | Capabilities | Best for | Latency | Status | Source |
|---|---|---|---|---|---|---|---|---|---|
| Anthropic: Claude Fable 5anthropic/claude-fable-5 | Anthropic | $1.45 / 1M tokens | $7.23 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Haiku 4.5anthropic/claude-haiku-4.5 | Anthropic | $0.145 / 1M tokens | $0.723 / 1M tokens | 200k | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Opus 4.5anthropic/claude-opus-4.5 | Anthropic | $0.723 / 1M tokens | $3.62 / 1M tokens | 200k | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Opus 4.6anthropic/claude-opus-4.6 | Anthropic | $0.723 / 1M tokens | $3.62 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | Anthropic | $0.723 / 1M tokens | $3.62 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | Anthropic | $0.723 / 1M tokens | $3.62 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Claude Opus 5anthropic/claude-opus-5 | Anthropic | $0.723 / 1M tokens | $3.62 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
| Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5 | Anthropic | $0.434 / 1M tokens | $2.17 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1000-3000ms | Catalog | Platform curated |
常見問題
Tool calling, structured JSON, enough context for the task, and instructions that stick. Everything else is secondary.
They generate long traces: planning text, tool results stuffed back into context, and retries. Cap steps and log token use per run.
No. Route planning or simple tool picks to cheaper models when quality allows, and reserve stronger models for hard steps.