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Solutions

Long context. Real applications.

A 1M-token context window is a single API parameter on our managed inference endpoints. Whole-codebase reasoning, multi-document analysis, and persistent agent memory — at the same hardware cost as conventional 8K serving.

What it costs

Long context, normal pricing.

Max context
1M
Llama 3.1 70B-1m and DeepSeek V3-1m endpoints.
Cost vs 8K
1.4×
Same model at 8K context; not 100x.
Quality (RULER)
99.1%
Of dense-attention RULER score.
Prefill throughput
3,840 tok/s
Single B200, 1M-token prefill.

Applications

What customers build with long context.

Whole-codebase reasoning

Repository-scale completion and analysis. Engineers point the model at a 600K-line monorepo and ask for the bug — the model has the whole answer in context.

Multi-document QA

Legal discovery, financial filings, medical record review. Hundreds of documents in a single prompt with citations back to source pages, not a RAG index.

Agent memory

Agents and assistants that retain a million tokens of conversation. Affordable enough to run continuously, accurate enough to use as the canonical memory layer.

Long-form generation

Book-length generation that maintains plot consistency across hundreds of pages. The model sees the whole story while it writes the next chapter.

Codebase migration

Whole-monorepo refactors and migrations. The model sees the call graph, the type system, and the tests in one prompt.

Compliance review

Audit a year of contracts, communications, or filings against a policy. The model produces a citation-backed report in one pass.

Long-context inference

Try a million-token prompt.

Free trial credits cover hundreds of long-context completions.