gpt-5.6-luna
$655.00 / month- Input cost
- $205.00
- Output cost
- $450.00
- Per 1,000 calls
- $13.10
- Pricing profile
- standard / short context
Source-tracked model comparison
How do GPT-5.6 Luna and Gemini 2.5 Flash costs compare for developer assistance?
Compare verified pricing and model limits for mid-volume developer assistance. The cost example uses a developer assistant backend workload and does not assume equal model quality.
Direct cost answer: gpt-5.6-luna is estimated at $655.00 per month and Gemini 2.5 Flash at $249.00 for 5,000 input tokens, 1,500 output tokens, and 50,000 monthly requests. Gemini 2.5 Flash is $406.00 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-5.6-luna | Gemini 2.5 Flash |
|---|---|---|
| Provider | OpenAI | |
| API model ID | gpt-5.6-luna | gemini-2.5-flash |
| Input / 1M | $1.00 | $0.30 |
| Cached input / 1M | $0.10 | $0.03 |
| Output / 1M | $6.00 | $2.50 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Maximum output | 128,000 tokens | 65,536 tokens |
| Accepted input | text, image | text, image, video, audio |
Example workload
Developer assistant backend cost scenario
5,000 input and 1,500 output tokens per request, 50,000 monthly requests, and 20% cached input.
Gemini 2.5 Flash
$249.00 / month- Input cost
- $61.50
- Output cost
- $187.50
- Per 1,000 calls
- $4.98
- Pricing profile
- standard / text
Cost result: Gemini 2.5 Flash is $406.00 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Which model handles the team's real code context more consistently?
- Do existing cloud and SDK choices reduce switching work?
- How much cached context is reusable in normal sessions?
What this estimate leaves out
The calculation does not compare coding quality and excludes indexing, execution sandboxes, and repository storage.
Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.
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