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，並在接入生產路由前比較月度成本。
Gemini 4 Argon via NextModel: 1M-token context; $2 / 1M tokens in / $10 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
Claude Haiku 5.5 via NextModel: 1M-token context; Starting at $0.1 / 1M tokens in / $0.5 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
Claude Sonnet 5.5 via NextModel: 1M-token context; $2 / 1M tokens in / $10 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
GPT‑6 Luna via NextModel: 1M-token context; Starting at $0.1 / 1M tokens in / $0.5 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
比較表
當你在縮小正式環境候選名單、建立兜底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 |
|---|---|---|---|---|---|---|---|---|---|
| Gemini 4 Argongoogle/gemini-4-argon | $2 / 1M tokens | $10 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 2974-10589ms | Catalog | Platform curated | |
| Claude Haiku 5.5anthropic/claude-haiku-5.5 | Anthropic | Starting at $0.1 / 1M tokens | $0.5 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1321-2017ms | Catalog | Platform curated |
| Claude Sonnet 5.5anthropic/claude-sonnet-5.5 | Anthropic | $2 / 1M tokens | $10 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1230-2392ms | Catalog | Platform curated |
| GPT‑6 Lunaopenai/gpt-6-luna | OpenAI | Starting at $0.1 / 1M tokens | $0.5 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1722-2681ms | Catalog | Platform curated |
| GPT‑6 Solopenai/gpt-6-sol | OpenAI | Starting at $2 / 1M tokens | $10 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1752-3715ms | Catalog | Platform curated |
| Claude Opus 5.5anthropic/claude-opus-5.5 | Anthropic | $4 / 1M tokens | $20 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1845-5119ms | Catalog | Platform curated |
| GPT-6 Astraopenai/gpt-6-astra | OpenAI | Starting at $10 / 1M tokens | $50 / 1M tokens | 1.1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 2408-7945ms | Catalog | Platform curated |
| Claude Fable 5.1anthropic/claude-fable-5.1 | Anthropic | $10 / 1M tokens | $50 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 3187-5152ms | 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.