GPT-5.6 Sol
$2,950.00 / month- Input cost
- $1,750.00
- Output cost
- $1,200.00
- Per 1,000 calls
- $5.90
- Pricing profile
- standard / short context
Source-tracked model comparison
Kimi K2.7 Code 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 classification and routing workload and does not assume equal model quality.
Direct cost answer: GPT-5.6 Sol is estimated at $2,950.00 per month and Kimi K2.7 Code at $492.50 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. Kimi K2.7 Code is $2,457.50 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 K2.7 Code |
|---|---|---|
| API access provider | OpenAI | Kimi API |
| Model creator | OpenAI | Moonshot AI |
| API model ID | gpt-5.6-sol | kimi-k2.7-code |
| Input / 1M | $5.00 | $0.95 |
| Cached input / 1M | $0.50 | $0.19 |
| Output / 1M | $30.00 | $4.00 |
| Context window | 1,050,000 tokens | 262,144 tokens |
| Maximum output | 128,000 tokens | Unavailable tokens |
| Accepted input | text, image | text |
Example workload
Classification and routing cost scenario
700 input and 80 output tokens per request, 500,000 monthly requests, and 0% cached input.
Kimi K2.7 Code
$492.50 / month- Input cost
- $332.50
- Output cost
- $160.00
- Per 1,000 calls
- $0.985
- Pricing profile
- standard
Cost result: Kimi K2.7 Code is $2,457.50 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
Quality and latency are not assumed equal across models; validate labels on a representative evaluation set.
Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.