Claude Haiku vs GPT-4o-mini

The 2024 matchup in the title is history. Here is the small-model tier as it stands in 2026.

At a glance

Pricing

All prices are per million tokens, input and then output, taken from the vendors' official pricing pages in 2026.

Anthropic's current small model is Claude Haiku 4.5 at $1.00 / $5.00 with a 200K context window. The Claude 3 Haiku this page was named for launched at $0.25 / $1.25, so Anthropic's small tier got four times more expensive per input token while moving much closer to mid-tier quality. Anthropic discounts batch processing by 50 percent and discounts cached input heavily, which matters for classification workloads that reuse a long shared prompt.

OpenAI's small tier is now a ladder. GPT-4o mini is still listed at $0.15 / $0.60. GPT-4.1 mini and nano run $0.40 / $1.60 and $0.10 / $0.40. GPT-5 mini and nano run $0.25 / $2.00 and $0.05 / $0.40. The newest pair, GPT-5.4 mini and GPT-5.4 nano, run $0.75 / $4.50 and $0.20 / $1.25. Cached input and batch discounts are available across the line.

Read those numbers with one caution: the newer small models on both sides are reasoning models, and reasoning tokens bill as output. A model with a low sticker price that thinks at length can cost more per task than a pricier model that answers directly.

When to pick Claude Haiku

Pick Haiku 4.5 when you are already building on Claude. If your main agent runs on a larger Claude model, using Haiku for subagent work, routing, and summarization keeps one vendor, one API surface, and one set of behavioral quirks to learn. The 200K context window is generous for a small model, and it supports extended thinking when a task needs more depth.

It is also the pick when your small-model tasks are less small than they look. In my experience the gap shows up on multi-step tool use and on instructions with several constraints at once, where Haiku 4.5 behaves like a scaled-down frontier model rather than a stripped one. You pay more per token than OpenAI's nano line and get fewer outright failures to catch and retry.

When to pick an OpenAI mini or nano

Pick the OpenAI side when the price floor is the point. At $0.05 per million input tokens, GPT-5 nano handles extraction, tagging, and routing at a cost where the model is effectively free relative to everything around it. The ladder itself is the feature: you can start on nano, move a workload up to mini when quality falls short, and never leave the API.

It is also the natural pick if your stack already runs on OpenAI or Azure. Migrating providers for a small-model workload rarely pays for itself; the token savings are real, but the eval and prompt-tuning time you spend to switch is real too.

What most comparisons miss

Most pages comparing these two models are frozen in 2024. Claude 3 Haiku against GPT-4o mini was a genuine like-for-like fight; the 2026 tiers are not. Anthropic consolidated on one more capable small model, and OpenAI fragmented into minis and nanos at several price points. Any comparison that ends with a single winner is answering last year's question.

Per-token price is also a weak proxy for per-task cost. Three things dominate real bills: how many output tokens the model spends (including hidden reasoning tokens), how much of your prompt you can cache, and whether the work can run through a batch API at half price. I have seen workload costs move far more from caching and batching than from switching models.

And small models churn fast. Both vendors replace this tier roughly yearly, and prices have moved in both directions. Whatever you pick, pin the model version, keep a small eval set, and re-run it when the next mini ships. The eval costs an afternoon and turns every future switch into a measurement instead of a guess.