gpt-5.4-nano
$67.10 / month- Input cost
- $54.60
- Output cost
- $12.50
- Per 1,000 calls
- $33.55
- Pricing profile
- standard / short context
Source-tracked model comparison
gpt-5.6-luna vs gpt-5.4-nano: which costs less for a practical API workload?
Compare verified pricing and model limits for routing within the GPT-5.6 family. The cost example uses a long-context research workload and does not assume equal model quality.
Direct cost answer: gpt-5.4-nano is estimated at $67.10 per month and gpt-5.6-luna at $333.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. gpt-5.4-nano is $265.90 lower under these assumptions. This does not identify a quality winner.
Tracked facts
Pricing and model limits
Prices are USD per 1M tokens under each model's verified default profile.
| Field | gpt-5.4-nano | gpt-5.6-luna |
|---|---|---|
| Provider | OpenAI | OpenAI |
| API model ID | gpt-5.4-nano | gpt-5.6-luna |
| Input / 1M | $0.20 | $1.00 |
| Cached input / 1M | $0.02 | $0.10 |
| Output / 1M | $1.25 | $6.00 |
| Context window | 400,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Accepted input | text, image | text, image |
Example workload
Long-context research cost scenario
150,000 input and 5,000 output tokens per request, 2,000 monthly requests, and 10% cached input.
gpt-5.6-luna
$333.00 / month- Input cost
- $273.00
- Output cost
- $60.00
- Per 1,000 calls
- $166.50
- Pricing profile
- standard / short context
Cost result: gpt-5.4-nano is $265.90 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Which model meets the required quality threshold on representative inputs?
- How do output length, retries, and caching change the measured cost?
- Which provider and deployment path fit the team's operational requirements?
What this estimate leaves out
Models whose tracked context window is below the scenario input are excluded from the compatible-model table.
Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.