Better results come from structure: a clear question, the right slice of the document, and an explicit output format.

How Much Can Claude 3.5 Sonnet Read at Once?

The 3.5 Sonnet line supports a context window in the 200K-token range, enough for a few hundred pages of text. That is a ceiling, not a target. Accuracy on a specific fact degrades as you add unrelated pages, and cost climbs with every token you send.

If your question is about chapter 4, send chapter 4.

How Do You Structure a Long-Document Prompt?

1. Put the document first, the question last — the model reads in order.
2. Scope it: “Answer only from sections 3 and 4.”
3. Request a shape: a table with columns Claim, Source Section, Confidence.
4. Ask it to quote the line it used for each claim.

The quote requirement is the single biggest accuracy jump. A model that must cite a line is far less likely to invent one.

What Are Common Long-Context Mistakes?

• Dumping the whole corpus when one section answers the question.
• No output schema, so answers arrive as loose prose.
• Trusting a summary without checking the quoted source.
• Reusing one giant thread across unrelated tasks, which muddies context.

When Should You Split Into Chunks Instead?

For very long corpora or repeated queries over one document, chunking pays off: split by section, run each through Claude 3.5 Sonnet, then ask it to reconcile the summaries. Costs less, and each answer stays grounded. For a single focused question about a mid-size report, one well-scoped prompt beats a pipeline.

Key Takeaways

• 3.5 Sonnet supports ~200K tokens of context — a ceiling, not a goal.
• Send the relevant section, not the whole document, for sharper answers.
• Require quoted source lines; it cuts hallucination on long texts.
• Chunk by section for repeated queries over large corpora.

Newer Claude models have since shipped, but 3.5 Sonnet remains common in production because it is cheap and predictable. Plenty of teams still run it as their daily driver.

What Is Claude 3.5 Sonnet Best At?

Three things. Long-context reading — feed it a 100-page report and ask for a structured summary. Writing that sounds less like a chatbot. And code, where it tends to produce readable diffs rather than clever one-liners you have to untangle.

It is weaker on heavy math and on tasks that need multi-step planning, where a reasoning model pulls ahead.

How Do You Get Started?

1. Use it at claude.ai on the free tier or via Anthropic’s API for automation.
2. Put your long document at the top of the prompt and ask for a specific deliverable.
3. For code, give existing style: “Match the style of the file below.”
4. Keep separate threads per task — context matters more than people expect.

Anthropic’s console also lets you compare a prompt across model tiers before you commit to one in an app.

How Much Does Claude 3.5 Sonnet Cost?

On claude.ai it is available on the free plan with limits; Pro is about $20/month with higher usage and access to more models. API pricing is per token and cheaper than the top tier, which is why it ends up in a lot of production pipelines where Opus would be wasteful.

When Should You Use a Different Model?

Pick Claude 3.5 Sonnet for writing, summarising and everyday code. For hard reasoning, frontier math, or when you need the absolute best answer and cost is secondary, a top-tier or reasoning model wins. A fair comparison is to run Claude 3.5 Sonnet and one newer model on the same five prompts before you pick.

Key Takeaways

• Claude 3.5 Sonnet (June 2024) is Anthropic’s mid-tier model: fast, cheap, long-context.
• Free tier on claude.ai with caps; Pro ~$20/month; API per-token below the top tier.
• Best for long-document reading, natural writing and readable code.
• Switch to a reasoning or top-tier model for hard multi-step problems.