Free DeepSeek API Pricing Calculator — Slide & Estimate Instantly
Compare DeepSeek V4 Flash, V4 Pro, and competitor model pricing. Adjust input/output token counts and cache hit rate assumptions to see real cost projections.
Higher hit rate = lower cost. DeepSeek caches at 12.5% of the original input price for hits.
If you do not already have billing exports, start from product traffic: requests per day, average prompt size, average response length, and expected cache reuse.
Count how many requests your app will send in a normal day, then multiply by 30. Separate peak campaigns from baseline traffic.
Use a few representative prompts to approximate average input and output tokens. For rough planning, consistency matters more than absolute precision.
If your system prompt and context are reused heavily, model a higher hit rate. If every request is unique, start closer to 0% and treat savings as upside.
These are charged at the model's standard input rate. Large prompts dominate cost when the cache hit rate is low.
When the prefix matches a previous request, DeepSeek charges the reduced cache-hit price instead of the full input rate.
Generated tokens are billed separately and can become the main cost driver in agentic or reasoning-heavy workflows.
Based on published API pricing as of July 2026. Actual costs may vary based on usage patterns and caching efficiency.
| Model | Input / 1M tokens | Output / 1M tokens | Cache Hit / 1M tokens | Notes |
|---|---|---|---|---|
| DeepSeek V4 Flash | ¥1.00 | ¥2.00 | ¥0.02 | — |
| DeepSeek V4 Pro | ¥3.00 | ¥6.00 | ¥0.02 | — |
| GPT-5.5 | ¥34.50 | ¥207.00 | ¥3.45 | — |
| GPT-5.4 | ¥17.25 | ¥103.50 | ¥1.73 | — |
| GPT-5.4 mini | ¥5.18 | ¥31.05 | ¥0.48 | — |
| Claude Fable 5 | ¥69.00 | ¥345.00 | ¥6.90 | — |
| Claude Opus 4.8 | ¥34.50 | ¥172.50 | ¥3.45 | — |
| Claude Sonnet 5 | ¥13.80 | ¥69.00 | ¥1.38 | — |
| Claude Haiku 4.5 | ¥6.90 | ¥34.50 | ¥0.69 | — |
If the workload is repetitive and latency-sensitive, DeepSeek V4 Flash usually sets the floor for cost planning.
The real question is not whether Pro is more expensive, but whether it cuts retries, human review, or downstream errors enough to justify the gap.
A model that looks expensive on list price can still make sense for narrow tasks. Re-run the calculator with your own token mix before locking the stack.
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