GPT-4o vs GPT-4.1: When the Cheaper Model Is Actually the Same Model
For eight months our default model in code was the string "gpt-4o". One Friday we changed it to "gpt-4.1". The next month's invoice was about 18% lower. Same product, no quality complaints, no rollback. The change took two minutes and the savings have shown up every month since.
The numbers, as of April 2026: GPT-4o is $2.50/1M input and $10.00/1M output. GPT-4.1 is $2.00/1M input and $8.00/1M output. About 20% cheaper across the board for text-only work.
What 20% looks like at scale
At a million requests a month, averaging 500 input + 500 output tokens, GPT-4o runs about $6.25 per million; GPT-4.1 runs about $5.00. Boring on its own. At 10 million requests a month it's a $12,500 line item that vanishes by changing one string in a config file.
Most teams default to GPT-4o because it's the familiar flagship and it was the smart default a year ago. For text-only workloads in 2026 it isn't anymore. If you're paying that 20% premium without using any of the multimodal features, you're paying for capability you aren't shipping.
When GPT-4o is still the right call
GPT-4o is OpenAI's multimodal model — text, images, audio, and vision in one API. If your application does image analysis, document OCR with visual elements, real-time audio, or anything where the input isn't plain text, stay on GPT-4o. GPT-4.1 doesn't have the same native multimodal coverage and the workarounds cost more than the 20% you'd save.
It's also the safe default for streaming chat where response feel has been tuned over time. If you're running customer-facing real-time chat and haven't benchmarked GPT-4.1 as a drop-in, run that test on a sliver of traffic first.
When GPT-4.1 is the better default
Document analysis and summarisation, code generation and review, structured-output API integrations, multi-turn text, classification and extraction at scale — for all of these GPT-4.1 is comparable to or slightly better than GPT-4o on quality and 20% cheaper. If the input isn't an image, start here.
The bigger lever: are you using a mini at all?
Switching between flagships is the small lever. The big one is whether you're routing simple tasks to a mini model in the first place. GPT-4o mini is $0.15/1M input and $0.60/1M output — 16× cheaper on input than GPT-4o. GPT-4.1 mini is $0.40/1M input and $1.60/1M output.
Routing even half your requests from a flagship to a mini — classification, extraction, simple Q&A — saves more than moving from GPT-4o to GPT-4.1 at the flagship tier ever will. The question isn't just which flagship to use; it's which requests actually need a flagship at all.
Before you decide what to route where, it helps to know which features and customers are driving your current spend. PerUnit breaks that down across customer, feature, and tier; the AI cost calculator lets you sanity-check the per-model math first.