Make.com has added GPT-5.4 nano and mini to its OpenAI connector, expanding the available model lineup for AI workflows. The release positions these new tiers as the default choice for high-volume, complex, and multimodal tasks where cost optimization matters more than peak model capability.
For teams running production AI workflows in Make.com, this matters more than it sounds. Operations cost is the silent killer of Make.com AI projects, and the new model tiers shift the math significantly.
What the new models actually offer
From OpenAI's positioning and Make.com's release notes, here's how the new tiers compare:
GPT-5.4 nano is OpenAI's smallest and fastest model in the GPT-5 family. Designed for high-throughput tasks where latency and cost matter most. Use cases: classification, extraction, simple summarization, intent detection.
GPT-5.4 mini is the mid-tier option. More capable reasoning than nano, still significantly cheaper than the flagship GPT-5.4. Use cases: content generation, multi-step reasoning that doesn't require deep analysis, agent tool use where speed matters.
Both support multimodal inputs (text + image), which puts them on par with their Anthropic Claude counterparts (Haiku and Sonnet respectively).
The cost math
This is where the real impact shows up. Let's compare three common Make.com AI workflows:
Email classification (10,000 emails/month):
- GPT-5.4 (full): ~$80/month in OpenAI charges + Make operations cost
- GPT-5.4 mini: ~$15/month
- GPT-5.4 nano: ~$4/month
Customer support drafting (1,000 tickets/month):
- GPT-5.4 (full): ~$30/month
- GPT-5.4 mini: ~$6/month
- GPT-5.4 nano: too small for this use case — would hallucinate
Content summarization (5,000 articles/month):
- GPT-5.4 (full): ~$120/month
- GPT-5.4 mini: ~$25/month
- GPT-5.4 nano: ~$8/month — works for short articles, weaker for long-form
The pattern: for many production workflows, mini and nano deliver 80-95% of the quality at 10-25% of the cost. That math changes which automations are economically viable to run at scale.
Which model to pick
A practical decision framework based on testing:
Use nano when:
- Task is classification, tagging, or boolean decisions ("is this spam?", "what category?")
- Input is short (under 500 words)
- Volume is high (thousands of calls per day)
- Latency matters more than reasoning depth
Use mini when:
- Task requires multi-step reasoning but not deep analysis
- Volume is medium-high (hundreds to thousands per day)
- Need good instruction following for content generation
- Agent tool use where each step is moderate complexity
Use full GPT-5.4 when:
- Task requires deep reasoning or complex analysis
- Volume is low (under 100 per day)
- Quality is more important than cost
- Customer-facing outputs where errors are costly
The hidden cost: testing time
The right model for a given workflow isn't always obvious. Most teams default to the most powerful model "just to be safe," then never test the cheaper alternatives even when they'd work fine.
Practical advice: when building a new AI workflow, test nano first. If it fails, move up to mini. If mini fails, move up to full. This "ladder up" approach finds the cheapest viable model rather than overspending by default.
Time investment: about 30-60 minutes per workflow to test across the three tiers with representative samples. Pays off across thousands of subsequent runs.
How this changes Make.com vs competitors
Two competitive angles:
vs Zapier: Zapier's "AI Actions" feature defaults to a specific model with limited control. Make.com's flexibility to pick model tiers per scenario is now a clear advantage — and the gap widens with more model options.
vs n8n: n8n offers the same model flexibility but with DIY setup. Make.com's native modules + new tier options give similar power with less setup time.
vs Anthropic-only workflows: If your team committed to Claude in Make.com earlier, the GPT-5.4 nano/mini release is worth a reconsideration for cost-sensitive workflows. Claude Haiku and Sonnet are still excellent, but having both options available means you can pick the best model per workflow.
What to do this week
If you run any AI workflows in Make.com at meaningful volume:
- Review your last month's OpenAI API costs in your account dashboard
- Identify the 3 most expensive workflows
- For each, test nano and mini against current production samples
- If quality holds at the lower tier, switch and document the savings
Conservative estimate: most teams will save 40-60% of their AI API costs from this audit alone, without sacrificing quality on most workflows.
If you need help optimizing your existing Make.com AI workflows for cost efficiency, our audit service includes operations consumption review.