Claude Haiku 4.5 vs Claude Opus 4.7 for Long Context

Long Context demands accurate retrieval and grounding across tens to hundreds of thousands of tokens, stable memory of prior content, and the ability to produce long, well-structured outputs without losing fidelity. In our testing the primary indicator is the long context score — both Claude Opus 4.7 and Claude Haiku 4.5 score 5/5 and are tied for 1st — so both models handle retrieval accuracy at 30K+ tokens in our suite. Practical differences come from raw context capacity and output headroom: Opus 4.7 offers a 1,000,000-token window and 128k max output tokens, while Haiku 4.5 offers 200,000 and 64k respectively. Supporting proxies show trade-offs: both models score 5/5 on tool calling and faithfulness (helpful for accurate extraction and tool-driven retrieval), Opus is stronger on constrained rewriting (4 vs 3) and creative problem solving (5 vs 4), and Haiku is stronger on multilingual tasks (5 vs 4). Cost is also a capability consideration: Haiku is priced at $1 per million input tokens and $5 per million output tokens, while Opus is $5 per million input and $25 per million output — meaning Opus delivers greater scale at substantially higher cost.

When to pick Claude Opus 4.7: • Ingesting and querying a 600k–800k token legal discovery corpus, or combining dozens of 100k-token technical manuals into a single retrieval session — Opus’s 1,000,000-token window and 128k outputs avoid truncation. • Generating very long reports or stitched transcripts where continuous, long-form output is required — Opus’s larger max output tokens reduce the need for chunking or multi-pass assembly. When to pick Claude Haiku 4.5: • Summarizing or extracting from documents in the 30k–200k token range (e.g., long research papers, books, or multi-document PRD collections) where Haiku’s 200k window and 64k outputs are sufficient and cost is important ($1 input / $5 output). • Multilingual long-context workflows (Haiku scores 5/5 multilingual vs Opus 4/5) such as extracting evidence from long non‑English documents. Additional nuance grounded in scores: both models tie at 5/5 for long context and 5/5 for tool calling and faithfulness, so for many retrieval tasks the practical difference is context headroom, constrained rewriting (Opus 4 vs Haiku 3), creative problem solving (Opus 5 vs Haiku 4), and cost.

For Long Context, choose Claude Haiku 4.5 if you need a cost‑efficient model for 30K–200K token retrieval, multilingual long-doc work, or lower-latency pipelines ($1 input / $5 output). Choose Claude Opus 4.7 if you must handle extreme scale (up to 1,000,000 tokens and 128k outputs), large single‑session corpora, or very long generated outputs and you accept higher costs ($5 input / $25 output).