Claude Opus vs. Sonnet vs. Haiku: For Business Use Cases

Anthropic does not sell one model. It sells three, and the differences between them matter more than most deal teams realize. Choosing the wrong Claude tier for a given task is like staffing a Managing Director on data entry or asking a first-year analyst to lead an IC presentation. The capability is there across the family. The question is where you point it.

Opus, Sonnet, and Haiku are not versions of the same product. They are distinct model families, each designed for a different weight class of work. The latest generations — Opus 4.6, Sonnet 4.6, and Haiku 4.5 — have widened the gaps between them while simultaneously raising the floor. Understanding those gaps is the difference between a well-architected AI deployment and an expensive one.

The Core Philosophy: The Finisher, the Workhorse, and the Prep Cook

The simplest way to think about the three tiers is in terms of where they sit in a workflow. Opus is the finisher — the model you bring in to produce the final deliverable, the polished deck, the complex model that needs to hold together under scrutiny. Sonnet is the workhorse — the model that handles the bulk of your day-to-day drafting, document work, and standard workflows, especially when you can give it detailed instructions. Haiku is the prep cook — the model that extracts, parses, classifies, and preprocesses large volumes of raw data so the bigger models have clean inputs to work with.

The model families have always followed this hierarchy, but the 4.x generation sharpened it. Opus got dramatically smarter at producing polished, complex output that holds together across long documents and multi-tab models. Sonnet became the reliable executor that follows detailed instructions precisely without the cost overhead of Opus. Haiku leapt from "useful for simple tasks" to a genuinely capable extraction and preprocessing engine. Each tier now occupies a more clearly defined lane than it did a year ago.

Round 1: Final Deliverables, Polish, and Complex Reasoning

This is where Opus earns its premium. When the output needs to be client-facing — the IC memo that goes to the investment committee, the LBO model with five scenarios and a waterfall, the board presentation that synthesizes a 500-page data room into a coherent narrative — Opus is the model that produces work you can actually send.

The difference is not just intelligence. It is judgment. Opus makes better decisions about what to emphasize and what to cut, how to structure an argument, where a sensitivity table needs tighter increments, and when a lease provision is ambiguous enough to flag rather than summarize. Give it a stack of unstructured source documents and ask for a polished deliverable, and the output reads like it was produced by someone who understood the assignment — not just someone who followed the instructions.

Sonnet can produce final deliverables, and for straightforward output it often does a clean job. But the gap shows up on complex work. A multi-scenario model built by Opus will have better internal consistency. An IC memo drafted by Opus will make sharper analytical choices. When the deliverable needs to survive scrutiny from a senior audience, Opus justifies the cost.

Haiku does not belong in this round. Its output is functional, not polished, and it will miss the nuances that separate a workable draft from a finished product.

Winner: Opus, decisively. For anything that leaves the firm or goes to a senior audience, this is the tier.

Round 2: Standard Workflows, Internal Drafting, and Document Work

Sonnet is built for the middle of the workflow — the work that makes up 70-80% of what a deal team actually does on any given day. Internal memo drafts, document markups, standard model builds, call summaries, data cleaning, formatting deliverables into templates. The kind of work where you know what you want, you can provide clear instructions, and you need reliable execution without paying for Opus-level reasoning on every task.

The key to getting the most out of Sonnet is instruction density. It follows detailed prompts with high fidelity. Tell it exactly what columns you want in a comp set, what structure your memo should follow, what formatting conventions your firm uses, and it executes precisely. It is the model equivalent of a strong junior hire who does exactly what you ask — and does it well — as long as the brief is clear.

This is also where Sonnet earns its place on document-heavy workflows. Reviewing a lease and extracting structured terms into a memo, redlining a draft against a prior version, pulling key provisions from an amendment stack and organizing them chronologically — Sonnet handles this work cleanly at roughly 2x the speed of Opus for 60% of the cost.

Opus can do all of this, but deploying it on standard internal workflows is overspending. The marginal quality improvement on instruction-driven tasks is not large enough to justify the price difference. Haiku is too thin for this category — it can follow simple instructions, but the output quality on unstructured document work or nuanced drafting drops off noticeably.

Winner: Sonnet, by a wide margin. The cost-adjusted productivity on instruction-driven workflows is unmatched in the family.

Round 3: Extraction, Data Parsing, and Preprocessing

Haiku 4.5 is the model you deploy before the other models ever see the data. Its role is not to produce final output — it is to prepare inputs. Extracting structured fields from a stack of PDFs, parsing thousands of line items from a GL export, classifying documents by type, pulling key metrics from a pipeline of OMs, normalizing messy data into clean tables that Sonnet or Opus can then work with.

This is where Haiku's economics become decisive. At $1 per million input tokens and $5 per million output tokens, a firm can run 10,000 documents through Haiku for what it would cost to process 200 through Opus. The use case is not complex reasoning — it is high-volume, structured extraction where accuracy on individual fields matters more than analytical nuance.

Practical applications: scanning a pipeline of OMs to extract deal metrics (NOI, cap rate, occupancy, WALT) into a standardized screening table. Classifying GL line items into CAM recovery categories before a Sonnet or Opus agent builds the reconciliation. Pulling tenant names, suite numbers, lease dates, and rent amounts from a folder of lease PDFs so a downstream model can perform the actual analysis on clean, structured data.

The intelligence floor has risen meaningfully. Haiku 4.5's extraction accuracy sits roughly where Claude 3.5 Sonnet was, which means it handles field-level extraction and classification with enough reliability for preprocessing work. Where it falls short is on ambiguous data — if a lease provision requires interpretation rather than extraction, or if a financial figure could reasonably be categorized two different ways, Haiku will pick one and move on without flagging the ambiguity. That is fine for preprocessing. It is not fine for final output.

Sonnet can do extraction work, but the economics do not justify it at volume unless the task genuinely requires its reasoning depth. Opus at volume is financially impractical for anything short of the highest-value analytical pipelines.

Winner: Haiku, and it is the only rational choice for preprocessing and extraction at scale.

Round 4: Data Privacy and Enterprise Security

This round is functionally a tie across the family. All three models operate under the same Anthropic enterprise security framework. On the API, customer data is not used for model training. The Claude Enterprise tier adds additional controls — SSO, audit logs, admin dashboards, and data retention policies — that apply equally regardless of which model tier you call.

The relevant consideration for deal teams is how each model is typically deployed. Opus and Sonnet, as the heavier models, are more commonly used in interactive workflows where a professional is directly engaged — reviewing output, iterating on deliverables, working in a session. Haiku, as the lightweight extraction model, is more commonly deployed in automated pipelines where documents flow through at volume. Both deployment patterns require attention to data handling, but the risk profile is different: interactive use involves fewer documents with more sensitive context, while pipeline use involves more documents with more structured, narrowly scoped extraction.

The security controls themselves — encryption in transit, data retention policies, access controls — are platform-level, not model-level. The choice of tier does not change your security posture.

Winner: Tie. Security is a platform decision, not a model decision.

Round 5: Cost Structure

The pricing hierarchy is straightforward. Per million tokens, Opus 4.6 runs $5 input / $25 output, Sonnet 4.6 at $3 / $15, and Haiku 4.5 at $1 / $5. For teams using the Claude.ai interface rather than the API, the Pro tier ($20/month) provides access to all three models with generous usage limits, and the Max tier ($100-$200/month) is built for heavy users.

The practical implication for deal teams: a firm that routes tasks intelligently across all three tiers can cut its AI spend by 60-70% compared to running everything through Opus. The firms getting the most value from Claude are the ones that treat model selection as a workflow decision — Haiku to prep the data, Sonnet to do the work, Opus to finish the deliverable.

Prompt caching (up to 90% savings on reused context) and the Batch API (50% discount for non-urgent work) are the primary levers for cost optimization, and they apply across all three tiers. For firms running Haiku at high volume, the Batch API in particular can cut already-low costs roughly in half.

Winner: Haiku on absolute cost. Sonnet on cost-adjusted value for professional workflows. Opus is expensive, but the cost is justified when the deliverable demands it.

The Verdict: Build a Pipeline, Not a Default

The deal teams getting the most out of Claude are not picking one tier and using it for everything. They are building workflows that move through all three:

• Haiku preprocesses the inputs — extracting data from raw documents, parsing and classifying large volumes of unstructured information, and producing clean structured tables that the larger models can work with.
• Sonnet does the work — taking those clean inputs and executing standard workflows: building models, drafting memos, marking up documents, following detailed instructions with high fidelity.
• Opus finishes the deliverable — taking Sonnet's output and applying the judgment, polish, and complex reasoning that turns a good draft into something you can send to an investment committee or a client.

The honest limitation across all three tiers is the same: they are general-purpose models. Opus does not inherently know how your firm formats an IC memo. Sonnet does not know the difference between a NNN lease and a modified gross lease until you tell it. Haiku can extract data from a thousand rent rolls, but it needs you to define every field and every edge case. Every session starts from zero.

Purpose-built AI coworkers — like those from Lumetric — are designed to close that gap. Instead of teaching a general model what a rent roll reconciliation looks like or how to structure a sensitivity table with 25bps increments, Lumetric's coworkers already understand the deliverable. Not the best general-purpose model at three different price points — the best analyst for the specific job, deployed as a specialized worker your team reaches by email.