# Claude vs GPT for process automation: which model to use in n8n By [Dobify](/en/about/) · Published 20 March 2026 · Updated 12 October 2026 · 8 min read - Claude - GPT - n8n Pick the model by task, not by vendor. Where both vendors offer a tier, list prices now match, so what matters is which tier each step needs: the small tier for scoring, classification and routing, the mid tier for writing and document parsing, and Claude Opus or OpenAI's mid tier for agents. Move up only when your own tests show the cheaper model falls short. Both vendors have shipped new model generations since this post first ran in March, so we redid the comparison on what they list today, 12 October 2026. Anthropic's current lineup is **Claude Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5** ([models overview](https://platform.claude.com/docs/en/about-claude/models/overview)). OpenAI's is **GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna** ([models](https://developers.openai.com/api/docs/models)). Both plug into n8n through native nodes in a few minutes. We don't compare synthetic benchmarks here (MMLU, GPQA and the like say little about real work). Instead we take 4 tasks from workflows we build for clients and say which tier we start each one on, and why. ## The price ladder List prices per million tokens, input / output ([Anthropic pricing](https://platform.claude.com/docs/en/about-claude/pricing), [OpenAI pricing](https://developers.openai.com/api/docs/pricing)): - **Top tier:** Claude Fable 5.1 $10 / $50; GPT-6 Astra $10 / $50. - **Anthropic's default:** Claude Opus 5.5 $4 / $20. - **Mid tier:** Claude Sonnet 5.5 $2 / $10; GPT-6.1 Sol $2 / $10. - **Small tier:** Claude Haiku 5.5 $0.10 / $0.50 (prompts up to 100,000 tokens); GPT-6 Luna $0.10 / $0.50 (inputs up to 272K tokens). Each vendor names a starting point: Anthropic suggests Claude Opus 5.5 for most workloads, OpenAI suggests GPT-6 Astra. The tasks below show where a lower rung is enough. ## Task 1: B2B lead scoring (0–100) Workflow: lead from LinkedIn Sales Navigator → Apollo enrichment → the model returns a score and a justification. Scoring is classification with a reason attached, which is what the small tier is for: Anthropic describes Haiku 5.5 as built "for high-volume, latency-sensitive tasks such as classification, extraction, and routing". Start on Haiku 5.5 or GPT-6 Luna, run 50 past leads with known outcomes through both, and keep the one whose scores your sales team agrees with. Move up a tier only if the small model misses the negative signals your team cares about (freelancers, students, retirees). Cost per lead (2,000 input / 300 output tokens) at list prices: about $0.00035 on Haiku 5.5 or GPT-6 Luna, $0.007 on Sonnet 5.5 or GPT-6.1 Sol, $0.014 on Opus 5.5, and $0.035 on Fable 5.1 or GPT-6 Astra. ## Task 2: Cold-email drafting from a LinkedIn profile Workflow: profile → the model writes 3 variants → A/B tested, best wins. Writing is where the mid tier earns its price over the small one. Sonnet 5.5 and GPT-6.1 Sol cost the same, so pick on output: give both the same 20 profiles and your best past emails as examples, and let the people who send the emails choose blind. If you sell outside English-speaking markets, run the test in the language you send in. ## Task 3: PDF invoice parsing and line-item extraction Workflow: email with PDF invoice → OCR → the model structures it into JSON → pushes to accounting. Broken JSON used to be the main failure here. It needn't be any more: both APIs can constrain output to a JSON Schema you supply ([Claude structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs), [OpenAI Structured Outputs](https://developers.openai.com/api/docs/guides/structured-outputs)). Turn it on. What's left is wrong values, such as a misread amount or a skipped line item, so add an n8n check that the line items add up to the invoice total and send mismatches to a person. For messy scans, test Sonnet 5.5, GPT-6.1 Sol and Opus 5.5 on 50 of your own invoices; the price gap per document is small next to one wrong booking. ## Task 4: Tool use / multi-step agent Workflow: an agent that autonomously calls APIs (CRM, calendar, Slack) to book a meeting with a lead, the pattern behind our [custom AI agents](/en/solutions/ai-agents/). Anthropic positions Fable 5.1 for "demanding reasoning and long-horizon agentic work", and OpenAI calls GPT-6 Astra its flagship "for complex reasoning and coding". A booking agent rarely needs that much: it makes a handful of tool calls against strict schemas. Start on Opus 5.5 or GPT-6.1 Sol, keep the tool schemas tight, and log every call so you can see where the agent invents a parameter. For agents that touch billing or payments, add a human approval step whichever model you pick. ## Rate limits Both vendors put your account in usage tiers that rise automatically as your spend and usage history grow, so check your current limits in each console before a launch that spikes traffic ([Anthropic rate limits](https://platform.claude.com/docs/en/api/rate-limits), [OpenAI rate limits](https://developers.openai.com/api/docs/guides/rate-limits)). One Claude detail matters in n8n: on most models, cached input tokens don't count toward the input-token rate limit. ## n8n integration n8n ships native [Anthropic](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-langchain.anthropic/) and [OpenAI](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-langchain.openai/) nodes. OpenAI's Assistants API, which this post used to count as a GPT advantage, was removed on 26 August 2026 ([OpenAI deprecations](https://developers.openai.com/api/docs/deprecations)); n8n's OpenAI node now offers Conversations instead. Prompt caching no longer separates the two either: a cache read costs a tenth of the input price or less on both (a twentieth on Claude Opus 5.5, Sonnet 5.5 and GPT-6.1 Sol), so a long system prompt is cheap on either. If you're still choosing the platform itself, settle that before the model: see [Make vs n8n vs Zapier](/en/blog/make-vs-n8n-vs-zapier-which-to-choose-2026/). ## Concrete recommendations - **Scoring, classification and routing:** the small tier, Claude Haiku 5.5 or GPT-6 Luna. - **Natural-language writing (emails, product copy, summaries), especially non-English:** the mid tier, Claude Sonnet 5.5 or GPT-6.1 Sol, picked by a blind test on your own examples. - **PDF / invoice / contract parsing:** the mid tier with structured outputs on; Claude Opus 5.5 for messy scans. - **Agents with many tool calls:** Claude Opus 5.5 or GPT-6.1 Sol to start; Fable 5.1 or GPT-6 Astra only when Opus or Sol fall short in your evals. - **Tight budget:** the small tier costs a twentieth of the mid tier per token, and it is the right default for every step that classifies rather than writes. ## n8n tip: multi-model routing In [production workflows](/en/solutions/automations/), don't settle on one model. Add a router: easy tasks (classification, scoring) → Haiku 5.5 or GPT-6 Luna. Medium (writing, extraction) → Sonnet 5.5 or GPT-6.1 Sol. Critical (outbound to clients, invoices) → Opus 5.5. At list prices the small tier is 20 times cheaper than the mid tier and 40 times cheaper than Opus 5.5, so every step you move down the ladder costs that much less. ### Building automations and not sure which model to pick? Our free audit looks at model routing in your workflows and shows which steps can move to a cheaper model. [Book an audit →](/en/free-audit/) ## Keep reading - [Make vs n8n vs Zapier in 2026: which to choose for your business](/en/blog/make-vs-n8n-vs-zapier-which-to-choose-2026/) - [How to build a WhatsApp bot that qualifies leads in 60 seconds](/en/blog/whatsapp-bot-that-qualifies-leads-in-60-seconds/) - [5 AI automations every law firm needs in 2026](/en/blog/5-ai-automations-every-law-firm-needs-2026/) ## Which process costs you the most? Thirty minutes, free. You leave with a concrete fix plan and what the process costs you. The plan is yours, even if you build it without us. [Book a free audit](/en/free-audit/) --- Claude and GPT for n8n workflows, on current models and list prices: lead scoring, cold emails, invoice parsing and agents, with a pick for each task. Source: https://dobify.dev/en/blog/claude-4-7-vs-gpt-5-for-business-automation-n8n/