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
Kimi K3 vs gpt-5.6-sol: which costs less for a practical API workload?
Compare verified pricing and model limits for API pricing and workload fit across providers. 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 Kimi K3 at $969.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. Kimi K3 is $696.00 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 Sol | Kimi K3 |
|---|---|---|
| API access provider | OpenAI | Kimi API |
| Model creator | OpenAI | Moonshot AI |
| API model ID | gpt-5.6-sol | kimi-k3 |
| Input / 1M | $5.00 | $3.00 |
| Cached input / 1M | $0.50 | $0.30 |
| Output / 1M | $30.00 | $15.00 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Maximum output | 128,000 tokens | Unavailable 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.
Kimi K3
$969.00 / month- Input cost
- $819.00
- Output cost
- $150.00
- Per 1,000 calls
- $484.50
- Pricing profile
- standard
Cost result: Kimi K3 is $696.00 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.