DeepSeek V4 Flash non-thinking
$54.57 / month- Input cost
- $37.07
- Output cost
- $17.50
- Per 1,000 calls
- $0.2183
- Pricing profile
- standard / cache miss
Source-tracked model comparison
Which has lower API cost for support triage: DeepSeek V4 Flash or Gemini 2.5 Flash-Lite?
Compare verified pricing and model limits for low-cost operational automation. The cost example uses a support ticket triage workload and does not assume equal model quality.
Direct cost answer: DeepSeek V4 Flash non-thinking is estimated at $54.57 per month and Gemini 2.5 Flash-Lite at $52.38 for 1,500 input tokens, 250 output tokens, and 250,000 monthly requests. Gemini 2.5 Flash-Lite is $2.19 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 Flash non-thinking | Gemini 2.5 Flash-Lite |
|---|---|---|
| Provider | DeepSeek | |
| API model ID | deepseek-v4-flash | gemini-2.5-flash-lite |
| Input / 1M | $0.14 | $0.10 |
| Cached input / 1M | $0.0028 | $0.01 |
| Output / 1M | $0.28 | $0.40 |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Maximum output | 384,000 tokens | 65,536 tokens |
| Accepted input | text | text, image, video, audio, pdf |
Example workload
Support ticket triage cost scenario
1,500 input and 250 output tokens per request, 250,000 monthly requests, and 30% cached input.
Gemini 2.5 Flash-Lite
$52.38 / month- Input cost
- $27.38
- Output cost
- $25.00
- Per 1,000 calls
- $0.2095
- Pricing profile
- standard / text
Cost result: Gemini 2.5 Flash-Lite is $2.19 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Which provider fits regional and procurement requirements?
- Do both models satisfy the required schema and latency target?
- How much traffic needs escalation after a real trial?
What this estimate leaves out
This estimate covers generation tokens only and does not assume equal routing accuracy between models.
Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.
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