Most production LLM traffic does not need a frontier model. Classification, extraction, routing, summarization, and tool-call argument generation all run fine on a small model at a fraction of the cost. The question is which small model. GPT-4o mini and Claude Haiku are the cheap tier of their respective families, and the default move is to pick whichever provider you already have a key for. That default leaves money and accuracy on the table, because the two models fail on different task types.
This is a comparison based on what each model does well in real pipelines, not MMLU deltas. The benchmark numbers are close enough that they do not tell you which one to ship. What tells you is task shape: how long the input is, whether the output must be strict JSON, how much instruction-following the prompt demands, and how tight your latency budget is.
GPT-4o mini
GPT-4o mini is OpenAI's small model in the GPT-4o family, positioned as the cheap, fast option for high-volume work. It handles text and image input, supports the same function-calling and structured-output APIs as the larger 4o models, and has a 128K token context window with 16K max output.
Pricing, as of early 2026: roughly $0.15 per 1M input tokens and $0.60 per 1M output tokens. Check OpenAI pricing before you budget, since OpenAI adjusts these regularly. Cached input tokens are billed at a lower rate, which matters if you reuse long system prompts across many calls.
Where it is strong: structured output. The response_format with a JSON schema enforces valid JSON at the decoding level, so you do not get malformed output that breaks a downstream parser. For extraction tasks where the schema is fixed and the input is short, this is the cleaner integration. Function calling is reliable, and the latency is low enough for interactive use.
Where it falls down: long-context reasoning and strict adherence to multi-step instructions. On inputs past roughly 16K to 32K tokens, recall of specific facts buried mid-document degrades faster than Haiku does. It also tends to be more literal. If your prompt says "return only the IDs that match all three conditions," it will occasionally return IDs that match two. For high-stakes filtering you need an eval harness, not trust.
Ownership status is straightforward: OpenAI owns and operates it, no separate weights, no self-hosting. You consume it through the OpenAI API or Azure OpenAI.
Claude Haiku
Claude Haiku is Anthropic's small model. The current generation, Claude Haiku 4.5, narrowed the gap with the mid-tier Sonnet models considerably on reasoning and instruction-following while staying in the cheap tier. It has a 200K token context window and supports tool use, vision, and the same Messages API as the larger Claude models.
Pricing, as of early 2026: roughly $1 per 1M input tokens and $5 per 1M output tokens for Haiku 4.5. That is meaningfully more expensive than GPT-4o mini per token. The older Claude 3 Haiku was cheaper, around $0.25 input and $1.25 output, and is still available if you want the rock-bottom price and can accept weaker reasoning. Confirm current Anthropic pricing and which Haiku version your SDK defaults to, because the version gap changes the cost math entirely.
Where it is strong: instruction-following and long-context work. Haiku 4.5 holds multi-step instructions better than GPT-4o mini in our testing on filtering and conditional-logic tasks, and its recall across long documents stays flatter as input grows. Prompt caching is aggressive and well-documented. If you have a 20K-token system prompt and call it thousands of times, cached input tokens drop the effective cost a lot, which partially closes the per-token gap with GPT-4o mini.
Where it falls down: raw price on short, high-volume calls. If your workload is millions of tiny classification calls with no reusable prefix, GPT-4o mini is cheaper per call and the accuracy difference may not justify the premium. Haiku's JSON output is good but enforced through tool-use schemas rather than a dedicated structured-output mode, so the integration pattern differs.
Ownership status: Anthropic owns and operates it. Available through the Anthropic API, Amazon Bedrock, and Google Vertex AI. No self-hosting.
Comparison table
| Dimension | GPT-4o mini | Claude Haiku 4.5 | noburn.dev |
|---|---|---|---|
| Input price (per 1M) | ~$0.15 | ~$1.00 | N/A (enforcement layer) |
| Output price (per 1M) | ~$0.60 | ~$5.00 | N/A |
| Context window | 128K | 200K | N/A |
| Structured output | Native JSON schema mode | Tool-use schemas | N/A |
| Long-context recall | Degrades past ~32K | Flatter across 200K | N/A |
| Instruction-following | Good, more literal | Stronger on multi-step | N/A |
| Prompt caching | Yes | Yes, aggressive | N/A |
| Self-hosting | No | No | Self-host the enforcement proxy |
| Cost control | Post-hoc dashboards | Post-hoc dashboards | Pre-flight, blocks before the call fires |
| Enforcement model | None | None | Estimate cost, block if over budget |
Prices are approximate and move. Verify against each provider's current pricing page before committing a budget.
Pick by task shape
The decision is not "which model is better." It is "which model fits this task."
Use GPT-4o mini when the input is short, the output is strict JSON against a fixed schema, and you are running high volume where the per-token price dominates. Extraction from short documents, intent classification, and routing all fit. The native structured-output mode and lower price make it the default for these.
Use Claude Haiku when the input is long, the prompt has conditional or multi-step logic the model must follow exactly, or you reuse a large system prompt across many calls and can lean on prompt caching. Document QA over long context, conditional filtering, and agent steps that chain tool calls fit here. The higher list price is real, so confirm caching actually lowers your effective rate before you assume the gap closes.
The honest answer for most teams: run both behind one interface, route by task type, and measure. LiteLLM or the Vercel AI SDK lets you swap models per call without rewriting the integration, so you can A/B the two on your actual traffic instead of trusting a benchmark.
The enforcement gap
Both providers give you usage dashboards. Both let you set a monthly spend cap that, when hit, stops your account from making calls. Neither gives you what a multi-tenant product actually needs: the ability to stop a specific user or project from spending before the call fires, without taking down everyone else.
That gap shows up the moment you put a small model in front of customers. A retry loop that re-prompts on every malformed response, a customer who pastes a 100K-token document into a per-call endpoint, an agent that recurses, any of these can run up a bill in minutes. The provider dashboard shows you the damage the next morning. The account-level spend cap, if you set one, cuts off your entire product when one tenant misbehaves. Neither is per-user, and neither is pre-flight.
Switching from Haiku to GPT-4o mini to save money does not close this gap. You are still billed for whatever fires, and you still find out after. The cheaper per-token rate just means the runaway bill takes slightly longer to hurt. The control you need is not a cheaper model, it is a check that runs before the request leaves your service and refuses it when the user or project is already over budget.
FAQ
Is Claude Haiku worth paying more than GPT-4o mini? It depends on task shape. For long-context work and strict multi-step instruction-following, Haiku 4.5's accuracy advantage can be worth the higher token price, especially with prompt caching lowering your effective rate. For short, high-volume classification with a fixed JSON schema, GPT-4o mini is cheaper and usually accurate enough.
Which one has better JSON output? GPT-4o mini has a native structured-output mode that enforces a JSON schema at decoding time, so you do not get malformed JSON. Haiku produces JSON through tool-use schemas, which is reliable but a different integration pattern. If strict schema conformance with zero parsing errors is the priority, GPT-4o mini's mode is the cleaner path.
Can I run either model self-hosted? No. Both are closed, provider-operated APIs. GPT-4o mini runs through OpenAI or Azure OpenAI; Claude Haiku runs through the Anthropic API, Amazon Bedrock, or Google Vertex AI. If self-hosting is a hard requirement you need an open-weight small model instead, which is a different comparison.
How do I keep a small-model workload from blowing the budget? Provider dashboards report spend after the fact and account-level caps cut off your whole product at once. To stop a single user or project before the call fires, you need pre-flight enforcement that estimates the cost client-side and blocks the request when that user is over budget. That is the enforcement layer, separate from the model choice.
Should I just use one model for everything? Only if your traffic is uniform. Most production workloads mix short and long inputs and strict and loose tasks, which means one model is suboptimal for part of it. Routing per task type behind LiteLLM or the Vercel AI SDK lets you use the cheaper or more accurate model where each wins, measured on your own traffic.
Where noburn fits in this stack
noburn does the one thing neither GPT-4o mini nor Claude Haiku does: it estimates the token cost of a request before the API call fires and blocks the request when the user or project is already over budget. The provider dashboards report spend after the call has billed; noburn refuses the call up front. It integrates at the SDK layer with OpenAI, Anthropic, LiteLLM, LangChain, LangGraph, and the Vercel AI SDK, so it sits in front of whichever small model you route to without changing your call sites. For multi-tenant products it enforces a separate budget per end-customer and meters per user, and Stripe passthrough billing lets you charge each customer for their own LLM usage. The free tier covers 50,000 requests per month. Documentation and SDKs are at noburn.dev/docs.