Compare long-context model APIs by window size, price, source, and when a big context is actually worth paying for.
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.
Podstawa źródła: NextModel curated catalog and OpenRouter context metadata when available. · Zaktualizowano 2026-07-01
Context
Zacznij od krótkiej listy, przetestuj prawdziwe prompty i porównaj miesięczny koszt przed routingiem produkcyjnym.
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.
Tabela porównawcza
Użyj tego widoku, gdy zawężasz shortlistę produkcyjną, budujesz politykę fallback albo porównujesz ekonomię modeli.
| 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.
Rankingi