DeepSeek V4 Pro
$126.26 / month- Input cost
- $117.56
- Output cost
- $8.70
- Per 1,000 calls
- $63.13
- Pricing profile
- standard / cache miss
Source-tracked model comparison
DeepSeek V4 Pro vs Kimi K3: 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: DeepSeek V4 Pro is estimated at $126.26 per month and Kimi K3 at $969.00 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. DeepSeek V4 Pro is $842.74 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 | DeepSeek V4 Pro | Kimi K3 |
|---|---|---|
| API access provider | DeepSeek | Kimi API |
| Model creator | DeepSeek | Moonshot AI |
| API model ID | deepseek-v4-pro | kimi-k3 |
| Input / 1M | $0.435 | $3.00 |
| Cached input / 1M | $0.0036 | $0.30 |
| Output / 1M | $0.87 | $15.00 |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Maximum output | 384,000 tokens | Unavailable tokens |
| Accepted input | text | 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: DeepSeek V4 Pro is $842.74 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.
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