Last verified 2026-02-11 (left) · 2026-03-06 (right)

Claude Haiku 4.5 vs GPT-5 mini — Pricing & Capability Comparison

Claude Haiku 4.5 charges $1.00 per million input tokens and $5.00 per million output tokens. GPT-5 mini comes in at $0.25 / $2.00. Context windows span 200K vs 200K tokens respectively.

TL;DR — Quick Comparison

✓GPT-5 mini is cheaper overall: $2.25 per 1M tokens (in+out) vs $6.00 for Claude Haiku 4.5 — saves $3.75 per 1M tokens
✓Input pricing: Claude Haiku 4.5 $1.00/1M vs GPT-5 mini $0.25/1M
✓Output pricing: Claude Haiku 4.5 $5.00/1M vs GPT-5 mini $2.00/1M
✓Context window: Claude Haiku 4.5 offers more (200K vs 200K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

Claude Haiku 4.5

$1.00

GPT-5 mini

$0.25

GPT-5 mini leads here

Output price (per 1M)

Claude Haiku 4.5

$5.00

GPT-5 mini

$2.00

GPT-5 mini leads here

Context window

Claude Haiku 4.5

200,000 tokens

GPT-5 mini

200,000 tokens

Claude Haiku 4.5 leads here

Cached input

Claude Haiku 4.5

$0.100

GPT-5 mini

$0.025

GPT-5 mini leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between Claude Haiku 4.5 and GPT-5 mini.

Choose GPT-5 mini if input tokens dominate your bill

GPT-5 mini has the lower input rate, which usually matters most for chat, RAG, classification, and long-prompt workflows where prompt volume stays much larger than generated output.

Choose GPT-5 mini if you generate long answers

GPT-5 mini is cheaper on output tokens, so it tends to win for report generation, coding assistance, reasoning traces, and any workflow where completions are long.

Choose Claude Haiku 4.5 if context size is the blocker

Claude Haiku 4.5 offers the larger published context window, which is more important than small pricing differences when you need to fit large files, long chats, or multi-document prompts into one request.

Cost comparison for 10K-token workloads

Side-by-side pricing for identical workloads (10,000 total tokens per request) across different distributions.

Scenario	Claude Haiku 4.5	GPT-5 mini	Claude Haiku 4.5 cached	GPT-5 mini cached
Balanced conversation 50% input · 50% output	$0.0300	$0.0112	$0.0255	$0.0101
Input-heavy workflow 80% input · 20% output	$0.0180	$0.0060	$0.0108	$0.0042
Generation heavy 30% input · 70% output	$0.0380	$0.0148	$0.0353	$0.0141
Cached system prompt 90% cached input · 10% fresh output	$0.0140	$0.0043	$0.0059	$0.0022

Frequently asked questions

Which is cheaper: Claude Haiku 4.5 or GPT-5 mini?

GPT-5 mini is cheaper for input tokens at $0.25 per 1M tokens compared to $1.00. For output, GPT-5 mini costs $2.00 per 1M tokens versus $5.00 for Claude Haiku 4.5.

What is the cost per 1M tokens for Claude Haiku 4.5?

Claude Haiku 4.5 pricing: $1.00 per 1M input tokens and $5.00 per 1M output tokens. Context window: 200,000 tokens.

What is the cost per 1M tokens for GPT-5 mini?

GPT-5 mini pricing: $0.25 per 1M input tokens and $2.00 per 1M output tokens. Context window: 200,000 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: Claude Haiku 4.5 costs $0.0010 input / $0.0050 output. GPT-5 mini costs $0.0003 input / $0.0020 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

Claude Haiku 4.5 offers 200,000 tokens (200K) versus 200K for GPT-5 mini.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: Claude Haiku 4.5 would cost approximately $20.00, while GPT-5 mini would cost $6.50. GPT-5 mini is more economical for this usage pattern.

Do these models support prompt caching?

Claude Haiku 4.5 supports prompt caching at $0.100 per 1M cached tokens, reducing costs for repeated context by up to 90%. GPT-5 mini supports caching at $0.025 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5 mini for cost-sensitive applications with high input volume. Choose Claude Haiku 4.5 if you need 200K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.