Claude Haiku 4.5 vs Claude Sonnet 4.6 for Long Context

What Long Context demands: retrieval accuracy at 30K+ tokens requires three technical capabilities: (1) a large usable context window and long max-output support so the LLM can ingest and reproduce large spans without truncation, (2) high faithfulness and retrieval precision to avoid hallucinated or incorrect extracts, and (3) robust tooling/formatting (structured outputs, tool calling) to map retrieved content into usable schemas. On these dimensions both Claude Haiku 4.5 and Claude Sonnet 4.6 score 5/5 for long_context and 5/5 for faithfulness and tool_calling in our tests, showing they both meet the core retrieval-accuracy requirement. Where they diverge is in infrastructure: Sonnet 4.6 provides a 1,000,000-token context_window and 128,000 max_output_tokens vs Haiku 4.5's 200,000 and 64,000, and Sonnet also scores higher on safety_calibration (5 vs 2) and creative_problem_solving (5 vs 4). Those differences matter for single-request workflows that must keep entire megabyte-scale archives in-context or emit very long synthesized outputs. Cost and latency tradeoffs also matter: Haiku is significantly cheaper per mTok (input: $1 vs $3; output: $5 vs $15 per mTok), so it’s preferable when you shard documents or need many low-latency calls.

Where Claude Sonnet 4.6 shines (practical):

• One-shot codebase comprehension: ingest a monolithic repository snapshot or concatenated design docs approaching 800K–1M tokens and extract accurate cross-file references — Sonnet’s 1,000,000-token window and 128,000-token outputs keep content in a single request and reduce chunking errors. Sonnet’s safety_calibration 5 also helps reject unsafe redactions.
• Large legal due diligence or M&A synthesis: produce consolidated findings and legally precise extracts across many long contracts without losing earlier context — Sonnet’s larger window reduces stitching-induced hallucinations.

Where Claude Haiku 4.5 shines (practical):

• Cost-sensitive indexing+retrieval pipelines: if you shard documents into 100K–200K token chunks and perform many quick retrievals, Haiku’s input/output costs (input $1 / mTok, output $5 / mTok) and lower latency make it cheaper to operate at scale while still scoring 5/5 on long_context in our tests.
• Interactive document browsing and iterative Q&A: when users expect snappy responses and you can keep each request under 200K tokens, Haiku delivers similar retrieval accuracy at a fraction of Sonnet’s cost and likely lower latency.

Grounded score differences used: both models score 5/5 on long_context and 5/5 on faithfulness and tool_calling; Sonnet exceeds Haiku on context_window (1,000,000 vs 200,000), max_output_tokens (128,000 vs 64,000), and safety_calibration (5 vs 2). Prices per mTok: Haiku input $1, output $5; Sonnet input $3, output $15.

For Long Context, choose Claude Haiku 4.5 if you need 5/5 retrieval accuracy with lower cost and lower latency and can keep requests within a 200,000-token window or shard documents. Choose Claude Sonnet 4.6 if you need single-request access to much larger corpora (up to 1,000,000 tokens), longer synthesized outputs (up to 128,000 tokens), or stronger safety handling.