We've been running Claude in production for over a year now. It powers our Invoice Importer — an automated pipeline that reads supplier invoices and creates Xero entries. It also runs Jarvis, our internal AI assistant that handles everything from email triage to school administration.
So when someone asks us "should I use Claude or GPT for my business automation?" — we're not guessing. We're drawing from thousands of real API calls, production incidents, and late-night debugging sessions.
Here's the honest answer: it depends on what you're building. And anyone who tells you otherwise is selling something.
Where We Use Claude (And Why)
Invoice Processing
Our Invoice Importer takes messy, real-world invoices — PDFs, scanned images, emails with attachments — and extracts structured data: line items, tax codes, account mappings, contact details. That data feeds directly into Xero as draft invoices.
We chose Claude for this because of one thing: reliability with structured output.
When you ask Claude to return JSON from an invoice, it returns valid JSON. Consistently. It follows the schema you give it. It doesn't hallucinate extra fields or randomly decide to wrap your output in markdown code blocks (a problem we hit repeatedly with GPT-3.5 and GPT-4 in early testing).
Claude's instruction-following is genuinely excellent. You tell it "return only JSON, no preamble, match this exact schema" — and it does. Every time. That matters enormously when you're processing hundreds of invoices and feeding the output directly into an accounting API.
The AI Assistant (Jarvis)
Jarvis is a more complex beast. It has access to email accounts, calendar, file systems, APIs, and a persistent memory system. It handles tasks like:
- Morning email briefings across multiple accounts
- School admissions reporting
- Form creation and webhook automation
- Smart home control
- Security monitoring
This is heavy tool-calling territory. Jarvis might need to read an email, check a database, create a Jotform, register a webhook, and send a confirmation — all in one flow.
Claude handles this well. Its tool calling is structured, predictable, and it rarely gets confused about which tool to call or what parameters to pass. When you give it a complex multi-step task, it plans and executes methodically.
Where GPT Shines (And We'll Admit It)
Speed
GPT-4o and GPT-4o-mini are fast. Noticeably faster than Claude for simple tasks. If you're building something latency-sensitive — a chatbot that needs to feel instant, an autocomplete feature, a real-time classification pipeline — that speed difference matters.
For our invoice processing, a 2-second response vs a 4-second response doesn't matter. For a live customer-facing chat? It absolutely does.
The Ecosystem
OpenAI's ecosystem is larger. More tutorials, more libraries, more Stack Overflow answers. If you're a solo developer building your first AI integration, GPT has a gentler on-ramp. The OpenAI Python library is mature, well-documented, and handles edge cases you didn't know existed.
Anthropic's SDK is good — and improving rapidly — but the community around it is still catching up.
Coding Assistance (Codex)
We actually use OpenAI's Codex model for heavy coding tasks — repository-wide refactors, feature implementation, PR reviews. For pure code generation, especially when you need an agent that can explore a codebase, run tests, and iterate, Codex is excellent.
This isn't a knock on Claude's coding ability (Claude writes great code). It's that OpenAI built Codex specifically for agentic coding workflows, and it shows.
The Real Trade-Offs
Cost
This is where it gets nuanced. Claude Opus (the most capable model) is expensive — roughly 3-4x the cost of GPT-4o per token. But Claude Sonnet, which handles most production tasks perfectly well, is competitively priced.
Our approach: use Opus for complex orchestration and decision-making, Sonnet for routine tasks like email processing and report generation. This keeps costs manageable without sacrificing quality where it matters.
GPT-4o-mini is hard to beat on price for simple tasks. If you're doing high-volume classification or extraction where "good enough" is the bar, it's the rational choice.
Reliability & Downtime
Both services have outages. We've been bitten by both. The difference is in how they fail.
Claude tends to fail cleanly — you get an error, you retry, it works. GPT has occasionally given us degraded responses during partial outages — technically a 200 response, but the output quality drops noticeably. That's harder to detect and handle programmatically.
For production systems, we always recommend building retry logic and quality checks regardless of which provider you choose.
Tool Calling
Claude's tool calling is more predictable. When you define a tool with a JSON schema, Claude sticks to it. We've had fewer issues with malformed tool calls, missing required parameters, or the model deciding to call tools in unexpected combinations.
GPT's function calling is powerful — especially with parallel function calls — but we've found it occasionally tries to be "clever" in ways that break deterministic workflows. It might combine two tool calls when you expected them sequentially, or infer parameters you wanted it to ask about.
For business automation, predictability beats cleverness every time.
Context Window & Long Documents
Claude offers up to 200K tokens of context (with Opus potentially going higher). GPT-4o tops out at 128K. For our use cases — processing multi-page invoices, reading through email threads, analysing documents — Claude's larger context window is a genuine advantage.
But context window size isn't everything. What matters is how well the model uses that context. Both can lose track of details buried in very long inputs. We chunk our inputs regardless.
What We'd Recommend
Choose Claude if:
- You need reliable structured output (JSON, XML, specific formats)
- Your workflow involves complex multi-step tool calling
- Instruction-following precision matters more than speed
- You're processing documents or long-form content
- You want the model to be cautious and ask rather than assume
Choose GPT if:
- Latency is critical (customer-facing, real-time)
- You need the cheapest option for high-volume simple tasks
- Your team is more familiar with the OpenAI ecosystem
- You're building coding tools or IDE integrations
- You want the largest community and most third-party integrations
Or do what we do — use both.
Our production stack uses Claude for orchestration, reasoning, and document processing. We use GPT (via Codex) for coding tasks. We use Gemini for image generation and long-document analysis. Each model has strengths, and a well-architected system plays to all of them.
The Bottom Line
The "Claude vs GPT" debate is mostly a distraction. The real question is: what specific task are you automating, and which model handles that task most reliably at an acceptable cost?
We landed on Claude as our primary model because our core products — invoice processing and business automation — reward precision, instruction-following, and structured output over raw speed. Your use case might point you in a different direction, and that's fine.
The worst choice is picking a model based on hype, benchmarks, or brand loyalty. Pick based on your production workload. Test with your actual data. Measure what matters to your business.
That's what we did. A year later, we're still happy with the choice — while keeping our options open.
Want to see Claude-powered automation in action? Try our Invoice Importer — it turns supplier invoices into Xero entries automatically.