Long context helps when you stuff contracts, exports, support history, or large files into the prompt. It also makes bills jump. Compare window size and input price together, and decide whether retrieval would be cheaper than stuffing the whole document every time.
출처 기준: NextModel curated catalog and OpenRouter context metadata when available. · 업데이트 2026-07-01
Context
먼저 후보 목록으로 시작한 다음 실제 프롬프트로 테스트하고 운영 라우팅 전에 월간 비용을 비교합니다.
GPT-6 Astra via NextModel: 1.1M-token context; Starting at $10 / 1M tokens in / $50 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
GPT-6.1 Sol via NextModel: 1.1M-token context; Starting at $2 / 1M tokens in / $10 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
Claude Fable 5.1 via NextModel: 1M-token context; $10 / 1M tokens in / $50 / 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.
비교표
운영 후보를 좁히거나 폴백 정책을 만들거나 모델 경제성을 비교할 때 사용합니다.
| Model | Provider | Input | Output | Context | Capabilities | Best for | Latency | Status | Source |
|---|---|---|---|---|---|---|---|---|---|
| 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 |
| GPT-6.1 Solopenai/gpt-6.1-sol | OpenAI | Starting at $2 / 1M tokens | $10 / 1M tokens | 1.1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 2489-8238ms | 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 |
| 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 Opus 4.6anthropic/claude-opus-4.6 | Anthropic | $5 / 1M tokens | $25 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1930-4195ms | 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 |
| Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | Anthropic | $3 / 1M tokens | $15 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1306-1903ms | 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 |
FAQ
No. Bigger windows help with big inputs. Cost, latency, retrieval design, and answer quality still decide whether it is a good idea.
When most of the document is irrelevant to each question. Pull the useful chunks, send less context, and keep a smaller model if quality holds.
Multiply average input tokens (including stuffed documents) by input price, then add output. Long inputs dominate the bill more often than people expect.