Gemini 3.8 Flash
$242.25 / month- Input cost
- $204.75
- Output cost
- $37.50
- Per 1,000 calls
- $121.13
- Pricing profile
- standard
Source-tracked model comparison
Gemini 3.8 Flash vs GPT-6 Astra: which fits a long-context API workload?
Compare verified pricing and model limits for current Google and OpenAI long-context API pricing. The cost example uses a long-context research workload and does not assume equal model quality.
Direct cost answer: Gemini 3.8 Flash is estimated at $242.25 per month and GPT-6 Astra at $3,230.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. Gemini 3.8 Flash is $2,987.75 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 | Gemini 3.8 Flash | GPT-6 Astra |
|---|---|---|
| API access provider | OpenAI | |
| API model ID | gemini-3.8-flash | gpt-6-astra |
| Input / 1M | $0.75 | $10.00 |
| Cached input / 1M | $0.075 | $1.00 |
| Output / 1M | $3.75 | $50.00 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Maximum output | 65,536 tokens | 128,000 tokens |
| Accepted input | text, image, video, audio | 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-6 Astra
$3,230.00 / month- Input cost
- $2,730.00
- Output cost
- $500.00
- Per 1,000 calls
- $1,615.00
- Pricing profile
- standard / short context
Cost result: Gemini 3.8 Flash is $2,987.75 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 workload's quality threshold?
- How do output length and the OpenAI long-context boundary change cost?
- Does limited GPT-6 Astra access affect the deployment decision?
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.