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
Source-tracked model comparison
GPT-6 Astra vs GPT-5.6 Sol: how do access, context, and API cost differ?
Compare verified pricing and model limits for current and previous OpenAI flagship API cost. The cost example uses a long-context research workload and does not assume equal model quality.
Direct cost answer: GPT-6 Astra is estimated at $3,230.00 per month and GPT-5.6 Sol at $1,292.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. GPT-5.6 Sol is $1,938.00 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-6 Astra | GPT-5.6 Sol |
|---|---|---|
| API access provider | OpenAI | OpenAI |
| API model ID | gpt-6-astra | gpt-5.6-sol |
| Input / 1M | $10.00 | $4.00 |
| Cached input / 1M | $1.00 | $0.40 |
| Output / 1M | $50.00 | $20.00 |
| Context window | 1,050,000 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 Sol
$1,292.00 / month- Input cost
- $1,092.00
- Output cost
- $200.00
- Per 1,000 calls
- $646.00
- Pricing profile
- standard / short context
Cost result: GPT-5.6 Sol is $1,938.00 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Does the workload need GPT-6 Astra access and capabilities?
- How does the 272,000-token pricing boundary change monthly cost?
- Does GPT-5.6 Sol still meet the quality requirement on representative inputs?
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.