Claude Haiku 4.5 vs GPT-4o-mini: A Real Cost Comparison by Task Type
GPT-4o-mini is 6.7x cheaper on input tokens than Claude Haiku 4.5. But cheaper per token is not the same as cheaper per useful output. Here is when each model wins.
GPT-4o-mini costs $0.15 per million input tokens. Claude Haiku 4.5 costs $1.00 per million input tokens. That is a 6.7x difference on input, and an 8.3x difference on output ($0.60/M vs $5.00/M). If you stop the analysis there, you will route everything to GPT-4o-mini and wonder why your outputs keep disappointing you on certain tasks. The real question is not which model is cheaper per token, but which model is cheaper per satisfactory completion.
This post breaks down the cost and quality tradeoffs between these two models by task type, so you can build routing logic that actually improves your unit economics without degrading output quality.
The Raw Numbers
Before getting into task-specific behavior, it helps to see the pricing side by side:
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window |
|---|---|---|---|
| GPT-4o-mini | $0.15 | $0.60 | 128k |
| Claude Haiku 4.5 | $1.00 | $5.00 | 200k |
| Claude Haiku 3.5 | $0.80 | $4.00 | 200k (retired from direct API) |
A note on Claude Haiku 3.5: it was slightly cheaper and was available directly through Anthropic's API, but it has since been retired from first-party access. It remains available through AWS Bedrock and Google Vertex AI if you are already on those platforms. For new direct API integrations, Haiku 4.5 is the current small Anthropic model.
On a simple 500-token input, 200-token output request, GPT-4o-mini costs roughly $0.000195 while Haiku 4.5 costs $0.00150. That is about 7.7x more expensive per call at those ratios. For a system running 1 million such calls per day, the daily difference is $1,305 vs $195. The cost difference is real and meaningful.
When GPT-4o-mini Wins
For short, structured tasks with well-defined outputs, GPT-4o-mini is the clear choice on cost without meaningful quality loss. This includes sentiment classification, entity extraction from short documents, intent detection, binary or categorical labeling, and simple data transformation where the schema is clear and the input is clean.
These tasks share a common property: the model needs to follow a clear template more than it needs to reason deeply. GPT-4o-mini handles instruction following well when the instructions are explicit and the input is short. The model is tuned for high-throughput, low-cost inference, and that tuning shows on tasks that match its strengths.
Consider a real-world classification pipeline: you receive customer support tickets and need to tag each one with a department, priority level, and whether it contains a complaint. The input is typically under 300 tokens, the output is a small JSON object, and the task repeats millions of times per month. At that scale, running on GPT-4o-mini versus Haiku 4.5 saves roughly $850 per million calls. Over a year with steady volume, that is a meaningful cost reduction with no quality penalty.
For extraction tasks on short, well-formatted documents, the same logic applies. If the schema is stable and the documents are clean, GPT-4o-mini performs reliably and at a fraction of the cost. This is the core insight behind routing decisions for cheaper models.
When Haiku 4.5 Is Worth the Premium
The picture changes significantly for three task categories: code generation, long-context processing, and complex multi-step reasoning.
On code generation, Claude Haiku 4.5 produces cleaner, more idiomatic code with fewer hallucinated APIs. This is not just a subjective observation. When you factor in retry costs, the number of calls needed to get a working function matters. If GPT-4o-mini requires two attempts to produce valid code that Haiku 4.5 returns in one, the effective cost per working output narrows considerably. On more complex coding tasks, functions with non-trivial logic, multi-file awareness, or uncommon libraries, the success rate difference becomes more pronounced. The premium starts to look like it pays for itself.
For long-context tasks, the context window difference matters directly. Haiku 4.5 supports 200k tokens. GPT-4o-mini supports 128k. If your application processes long documents, extended conversation histories, large codebases, or dense RAG retrieval contexts, you may simply hit the GPT-4o-mini ceiling on some inputs. Beyond the hard limit, models tend to degrade in coherence as they approach their context window limits. Having 72k additional tokens of headroom is not trivial.
There is also a cost calculation that catches people off guard with long-context tasks. When you are sending 80,000 tokens of context per call, the input token count dominates total cost. At 80k input tokens, GPT-4o-mini costs $0.012 per call and Haiku 4.5 costs $0.080 per call. That is still a 6.7x gap, but the absolute dollar amounts are higher, which makes the quality question more important. If Haiku 4.5 produces a better answer on a complex document analysis task, the additional $0.068 per call may be worthwhile depending on what you are using the output for.
Multi-step reasoning tasks, questions that require the model to build intermediate conclusions before reaching a final answer, also tend to favor Haiku 4.5. The model's reasoning architecture is more robust on tasks that require holding multiple constraints simultaneously. This includes certain instruction-following scenarios where GPT-4o-mini has a tendency to drop constraints as prompt complexity increases.
The Tool Use Cost Factor
Tool use (function calling) introduces a separate cost dimension. Both models support structured function calling, but the number of tokens consumed by tool definitions adds up. A schema with 5 tool definitions might add 600-900 tokens to every call. At high volume, that overhead becomes significant regardless of which model you use. For a detailed look at how tool use inflates API costs, see the hidden cost of function calling.
What matters for the Haiku vs GPT-4o-mini comparison here is that tool-heavy pipelines may close the quality gap on GPT-4o-mini faster than expected. When models need to select the right tool, pass correct arguments, and handle multi-turn tool interactions, more capable models tend to require fewer retries. The retry cost, measured in tokens and latency, often narrows the apparent price advantage.
A Practical Routing Framework
Given the above, a reasonable routing rule for production systems looks like this. Send to GPT-4o-mini when the task is classification, extraction, or tagging with a schema under 1,000 output tokens and input under 20,000 tokens, the prompt is short and the instructions are explicit, and quality on a test set shows accuracy above your threshold. Route to Haiku 4.5 when the task involves code generation, the input exceeds 50,000 tokens, the task requires multi-step reasoning or constraint tracking, or when retries on GPT-4o-mini are consistently eating into the cost advantage.
The threshold for switching is not fixed. It depends on your acceptable error rate and the cost of a bad output in your specific application. A document summarization tool where users can re-run at will has different tolerance than a code generation assistant where broken code blocks a developer's workflow.
PromptUnit's routing layer lets you define these thresholds as rules or as learned policies from your own production data, routing individual requests to the appropriate model based on characteristics like token count, task type tags, or confidence scores from a classifier. The cost difference between these two models is large enough that getting routing right has a direct impact on your monthly bill.
Most teams building at scale end up using both models, not choosing one. The cost structure of modern LLM APIs rewards routing specificity. Teams that route everything to one model are leaving money on the table on one end and accepting avoidable quality degradation on the other. Cutting OpenAI API costs follows the same principle: the biggest savings come from matching the model to the task, not from negotiating rates.
Start with your highest-volume task type. Benchmark both models on a sample of real production inputs with your actual prompts. Measure accuracy, retry rate, and output length. Then calculate the true cost per satisfactory output, not cost per token. That number will tell you where to route.
Ready to build model routing that tracks cost per task type in production? Try PromptUnit to instrument your LLM calls and surface where switching models saves the most.