gpt-5.6-sol
$1,665.00 / month- Input cost
- $1,365.00
- Output cost
- $300.00
- Per 1,000 calls
- $832.50
- Pricing profile
- standard / short context
Source-tracked model comparison
How do GPT-5.6 Sol and Gemini 2.5 Pro costs compare for long-context research?
Compare verified pricing and model limits for large-context research workloads. The cost example uses a long-context research workload and does not assume equal model quality.
Direct cost answer: gpt-5.6-sol is estimated at $1,665.00 per month and Gemini 2.5 Pro at $441.25 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. Gemini 2.5 Pro is $1,223.75 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 | gpt-5.6-sol | Gemini 2.5 Pro |
|---|---|---|
| Provider | OpenAI | |
| API model ID | gpt-5.6-sol | gemini-2.5-pro |
| Input / 1M | $5.00 | $1.25 |
| Cached input / 1M | $0.50 | $0.125 |
| Output / 1M | $30.00 | $10.00 |
| 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, pdf |
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.
Gemini 2.5 Pro
$441.25 / month- Input cost
- $341.25
- Output cost
- $100.00
- Per 1,000 calls
- $220.63
- Pricing profile
- standard / short context
Cost result: Gemini 2.5 Pro is $1,223.75 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Does the full input fit within each tracked context window?
- How are citations and source coverage evaluated?
- Do provider-specific long-context rates change the expected total?
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.
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