★ Head-to-head
Claude Haiku 5.5 vs GPT-6 Luna: API Cost Comparison
The cheapest current models from Anthropic and OpenAI, both aimed at high-volume routing, extraction and short replies.
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- Updated 7 Nov 2026
| Side by side | Claude Haiku 5.5 Anthropic | GPT-6 Luna OpenAI |
|---|---|---|
| Input / 1M | $0.10 | $0.10 |
| Output / 1M | $0.50 | $0.50 |
| Cached input / 1M | $0.01 | $0.01 |
| Batch discount | 50% off | 50% off |
| Context window | 1M tokens | 1.05M tokens |
| Long-prompt surcharge | Above 100K input | Above 272K input |
| Tier | Small / fast | Small / fast |
Workload
Repeated system prompt or docs
Jobs that can wait hours
Models
$ per 1M tokens, input / output
Monthly cost · 30 days
- Cheapest
- $105
- Claude Haiku 5.5
- Priciest
- $105
- GPT-6 Luna
- Spread
- 1.0×
- priciest vs cheapest
| Per 1K users / mo | |||
|---|---|---|---|
Claude Haiku 5.5 | $0.00035 | $105 | $52.50 |
GPT-6 Luna | $0.00035 | $105 | $52.50 |
Where the money is
Caching half your input on GPT-6 Luna (a shared system prompt or document) would cut about $27.00/month.
You're looking at ~$105/month.
Most AI products I audit can cut that 30–60% with routing, caching and shorter prompts.
Cost at three traffic levels
Same request on both: 2,000 input tokens and 300 output tokens, no caching, no batch, 30-day month.
| Requests / day | Claude Haiku 5.5 | GPT-6 Luna | Difference |
|---|---|---|---|
| 1,000 | $10.50 | $10.50 | $0 |
| 10,000 | $105 | $105 | $0 |
| 100,000 | $1,050 | $1,050 | $0 |
By request type:
| Per 1K calls | Claude Haiku 5.5 | GPT-6 Luna |
|---|---|---|
| Classification or routing call | $0.0450 | $0.0450 |
| Support chat reply | $0.2750 | $0.2750 |
| RAG answer over your docs | $0.8000 | $0.8000 |
| Long document summary | $4.50 | $4.50 |
When to pick which
- Input tokens cost the same on both ($0.10 per 1M).
- Output tokens cost the same on both ($0.50 per 1M).
- Cache reads cost the same on both ($0.01 per 1M).
- Both offer 50% off for batch processing.
- GPT-6 Luna has the larger context window (1.05M tokens vs 1M tokens).
- Claude Haiku 5.5 bills the whole request at a higher rate once input passes 100,000 tokens.
- GPT-6 Luna bills the whole request at a higher rate once input passes 272,000 tokens.
Claude Haiku 5.5: High-volume classification, routing, extraction and short replies where cost per call matters most.
GPT-6 Luna: OpenAI's cheapest current model. Bulk tagging, extraction, routing and lightweight chat.
Verdict
It's a wash on price
On list price they are a wash for a typical request. Decide on quality for your task, latency, and which caching model fits your prompts. Then run both on 50 of your real prompts and compare answers and billed tokens side by side.
How the math works
Every provider bills input tokens (your prompt, system prompt and retrieved documents) and output tokens (the reply) separately, per million. Output is usually 4–5× the input price, so chatty apps are output-heavy and RAG apps are input-heavy.
Two adjustments are applied automatically. Introductory prices (currently on some Gemini models) are used until their end date. Long-prompt surcharges kick in when your average input crosses the provider's threshold: 272K tokens on current OpenAI models, 100K on Claude Haiku 5.5.
Not modelled: cache-write premiums, priority or fast tiers, regional surcharges, image and audio tokens, and reasoning tokens a model spends before answering (they bill as output, so add them to your output estimate if you use extended thinking).
Questions
Which is cheaper, Claude Haiku 5.5 or GPT-6 Luna?
Can I use both Claude Haiku 5.5 and GPT-6 Luna?
How often do Anthropic and OpenAI change prices?
Sources
All prices are list prices from each provider's own documentation, standard tier, in USD, last verified on 7 Nov 2026. Providers change prices often; if a number here disagrees with the source, the source wins.
- Claude Haiku 5.5platform.claude.com
- GPT-6 Lunadevelopers.openai.com
More from the workbench
Fine print
Tools: on the house.
Engineer: by the hour.
Knowing your AI bill was free. Cutting it in half isn't, but I'm cheaper than the overspend.
I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.
