Claude Haiku vs GPT-4o Mini Cost Showdown

Written by Michael Lip · Solo founder of Zovo · $400K+ on Upwork · 100% JSS Join 50+ builders · More at zovo.one

GPT-4o mini costs $0.15/$0.60 per million tokens. Claude Haiku 4.5 costs $1.00/$5.00. On raw price, GPT-4o mini is 6.7x cheaper on input and 8.3x cheaper on output. That is not a rounding error. So why would anyone use Haiku?

Because price per token is not price per result. The cheapest model is the one that gives you correct output in the fewest attempts at the lowest total cost. Sometimes that is GPT-4o mini. Sometimes it is not.

The Setup

The budget tier comparison as of April 2026:

Metric Claude Haiku 4.5 GPT-4o mini
Input per MTok $1.00 approximately $0.15
Output per MTok $5.00 approximately $0.60
Context window 200K tokens 128K tokens
Max output 64K tokens approximately 16K tokens
Batch input $0.50/MTok approximately $0.075/MTok
Batch output $2.50/MTok approximately $0.30/MTok
Cache read $0.10/MTok different system

GPT-4o mini wins on every price line. That much is indisputable. The question is whether price per token translates to price per successful outcome for your specific workload.

The Math

Scenario 1: High-volume classification (10K calls, 500 input + 50 output tokens)

Claude Haiku 4.5:

GPT-4o mini:

GPT-4o mini costs $1.05 vs Haiku’s $7.50. That is 7.1x cheaper for the same classification task assuming equal accuracy.

Scenario 2: Document analysis requiring 150K context

Claude Haiku 4.5 (handles 200K context):

GPT-4o mini (caps at 128K context): Cannot process this request at all. The alternative is GPT-4o at $2.50/$10.00 per MTok:

Haiku is 2.5x cheaper than the GPT-4o fallback because GPT-4o mini simply cannot handle the context length. At 10,000 calls, that difference is $1,600 vs $3,950, saving $2,350 with Haiku.

Scenario 3: Long-form generation needing 30K output tokens

Claude Haiku 4.5 (64K output limit): Single call, $0.15 output cost GPT-4o mini (approximately 16K output limit): Needs 2 calls minimum with context management overhead. Total cost may exceed Haiku despite lower per-token rate because you pay for input tokens twice.

The Technique

The decision framework for choosing between these budget models requires evaluating four dimensions:

Dimension 1: Task complexity. For well-defined, simple tasks like binary classification, sentiment labels, or entity extraction, GPT-4o mini’s quality is typically sufficient and its 7x price advantage is a clear win. For tasks requiring nuanced judgment, following complex instructions, or maintaining consistency across long outputs, benchmark both models on your actual data before deciding.

Dimension 2: Context requirements. If any requests need more than 128K tokens of context, GPT-4o mini is eliminated. Haiku supports 200K tokens. This single dimension overrides all pricing considerations.

Dimension 3: Output length. Haiku generates up to 64K output tokens per request. GPT-4o mini caps around 16K. For tasks producing more than 16K tokens, GPT-4o mini requires multiple calls with additional input token costs and orchestration complexity.

Dimension 4: Volume economics. At 100 calls per month, the cost difference between Haiku and GPT-4o mini is negligible (dollars, not hundreds). At 1M+ calls per month, the 7x gap translates to hundreds or thousands of dollars. Volume determines whether the price difference matters at all.

Apply Claude’s caching to narrow the gap: Haiku cache reads cost $0.10/MTok, which is only 33% cheaper than GPT-4o mini’s standard input rate of $0.15/MTok. Caching helps, but it does not fully close the 6.7x input price gap.

The Tradeoffs

GPT-4o mini wins when:

Claude Haiku wins when:

The honest assessment: For pure cost optimization on simple tasks at high volume, GPT-4o mini at $0.15/$0.60 is extremely hard to beat. Haiku is not the cheapest option for straightforward work. It is the cheapest capable option for workloads that exceed what mini can handle.

Implementation Checklist

  1. Profile your tasks by complexity tier and identify which are truly simple
  2. Check context length requirements for every task category
  3. Run quality benchmarks on 500 samples with both models side by side
  4. Calculate cost at your actual monthly volume for the top candidate
  5. Consider a split routing strategy: GPT-4o mini for simple tasks, Haiku for everything else
  6. Factor in Haiku batch pricing at $0.50/$2.50 for async workloads

Measuring Impact