A direct cost comparison between two of the most-considered LLM APIs in 2026. Headline rates, per-call math, and the trade-offs that matter beyond price.
On a standard 1,500-input / 500-output token workload, DeepSeek V3.2 comes out about 62% cheaper than GPT-5.4 Nano. At 10,000 calls per month, that translates to roughly $5.75 in monthly savings — small in absolute terms at low volume, but it compounds linearly. At 1 million calls per month, the same percentage gap means $575 in savings.
But the ranking can flip depending on your input-to-output ratio. If your prompts are very long and your responses are short (think: document Q&A, classification, extraction), the input rate dominates the cost. If your application generates long outputs (think: content generation, code, summarization), the output rate is what matters.
| Metric | DeepSeek V3.2 | GPT-5.4 Nano |
|---|---|---|
| Vendor | DeepSeek | OpenAI |
| Input price per M tokens | $0.14 | $0.20 |
| Output price per M tokens | $0.28 | $1.25 |
| Context window | 128K | 1M |
| Output/input ratio | 2.0× | 6.2× |
| Cost per typical call | $0.0004 | $0.0009 |
| Cost per 10,000 calls | $3.50 | $9.25 |
Price is a starting point, not the conclusion. DeepSeek V3.2 and GPT-5.4 Nano target different latency profiles, use different tokenizers (which materially affects effective cost on non-English text), and behave differently on the kinds of tasks you actually run in production. The numbers above give you the pure dollar comparison; your evaluations should give you the quality comparison.
A common production pattern in 2026 is to not choose between them at all. Instead, route by request difficulty: a cheaper, faster model handles 70–80% of incoming requests, and the more capable (and expensive) model gets the hard cases. This setup typically beats single-model deployments on both cost and quality, regardless of which two specific models are in play.
Both DeepSeek and OpenAI support prompt caching (cached input bills at approximately 10% of the standard rate) and batch processing (50% off for 24-hour SLA workloads). These discounts can completely swing the comparison. If one model has a much larger cacheable prefix in your specific setup — for instance, a long system prompt that stays constant — it can become cheaper in practice even if its headline rate is higher.
Run the math with your actual workload, not the marketing benchmarks. The calculator on the homepage lets you do this — paste your real prompts and see what happens.
For workloads where price is the deciding factor and quality differences are marginal, DeepSeek V3.2 is the better choice on this comparison. For workloads where the GPT-5.4 Nano quality bump genuinely improves downstream outcomes (fewer retries, better extraction accuracy, less human review), the price difference might be worth paying. Measure it on your own data before committing.