DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens on the official DeepSeek API. Across third-party hosts, the same model prices anywhere from $0.20 to $0.57 per million input tokens, and the cheapest list price is frequently not the cheapest real-world option once cache discounts enter the math.
This page is the pricing reference: official rates, the full provider comparison, and how to compute what your actual workload will pay. For why the same model prices this differently at all, see Why Token Prices Differ.
What DeepSeek V3.2 is, briefly
V3.2 replaced both DeepSeek V3 and R1 with a unified model that handles chat and reasoning at a single price point, with a 163K context window. It is one of the most widely hosted open-weight models in production, which is exactly why its pricing is worth a dedicated page: open weights mean many competing sellers, and many sellers mean a price spread worth shopping.
DeepSeek V3.2 official API pricing
| Token type | Price per 1M tokens |
|---|---|
| Input | $0.28 |
| Input, cache hit | $0.028 |
| Output | $0.42 |
The number that separates the official API from most hosts is the cache line: a 90 percent discount on cached input tokens. For workloads with heavy prompt reuse, this single term changes the provider ranking, as shown below.
DeepSeek V3.2 pricing by provider
Published rate cards across major hosts:
| Provider | Input $/1M | Output $/1M | Cached input $/1M |
|---|---|---|---|
| GMI | $0.20 | $0.32 | — |
| DeepInfra | $0.26 | $0.38 | — |
| Novita AI | $0.269 | $0.40 | $0.135 |
| SiliconFlow (FP8 endpoint) | $0.27 | $0.42 | — |
| DeepSeek official | $0.28 | $0.42 | $0.028 |
| Volcengine | $0.28 | $0.42 | $0.056 |
| Fireworks AI | $0.30+ | $0.45+ | — |
| Full observed range across endpoints | $0.18 – $0.57 |
Notes that matter as much as the prices: the SiliconFlow budget endpoint serves an FP8-quantized version, not full precision. Alibaba and ByteDance-hosted endpoints cap context at 131K rather than the full 163K. A cheaper endpoint that serves a different precision or a smaller context is not the same product at a lower price.
What a real workload actually pays
List prices mislead because workloads are a mix of input, output, and cached tokens. Take a production assistant doing 10M input and 1M output tokens per day:
| Provider | Daily | Monthly | Annual |
|---|---|---|---|
| GMI (cheapest list) | $2.32 | $69.60 | $847 |
| DeepSeek official | $3.22 | $96.60 | $1,175 |
On raw list price, the cheapest host saves 28 percent. Now give the same workload a 60 percent cache hit rate on input, common for agents and RAG systems with stable system prompts:
| Provider | Daily cost with 60% cached input |
|---|---|
| DeepSeek official ($0.028 cached) | $1.71 |
| GMI (no cache discount) | $2.32 |
The ranking flips. The "expensive" official API becomes 26 percent cheaper than the cheapest list price on the market, because 6M of the 10M daily input tokens bill at $0.028 instead of $0.20 or more. This is the single most common mistake in provider selection: comparing input list prices when cache terms dominate the actual bill.
The general rule: compute your blended price, the weighted cost across your real input, output, and cache mix. This is the logic behind the Standard Blended Price on the Mercatus Token Index, which tracks per-model blended pricing across providers daily.
How to choose a V3.2 provider
- Heavy prompt reuse (agents, RAG, chat with long system prompts): official API or another host with strong cache terms, almost regardless of list price.
- One-shot workloads with little repeated context (batch processing, document extraction): cheapest list price wins; GMI-tier hosts at $0.20 input.
- Quality-sensitive workloads: confirm full-precision serving; skip FP8 endpoints or benchmark them against your evals first.
- Long-context workloads: confirm the host serves the full 163K window; several cap at 131K.
- Latency-sensitive: the fastest hosts charge a premium; whether it's worth it is a throughput question, not a price question.
Where V3.2 pricing goes from here
Open-weight model pricing has moved one direction: down, as hosting competition compounds. But the spread between hosts persists, because token prices are posted rather than traded; no venue forces the gaps closed. That structure, and what changes it, is covered in How a Token Exchange Works. Until then, the spread is the buyer's problem and the shopper's opportunity: on this model it's a 28 to 65 percent cost difference depending on workload shape.
Serving costs also set a floor. Every V3.2 token price traces back to GPU-hour economics, the cost-per-token chain from hardware cost through throughput to price. Hosts running paid-off or cheap hardware can go lower than hosts on rented hyperscaler capacity, which is a large part of why the table above looks the way it does.
Frequently asked questions
How much does DeepSeek V3.2 cost per million tokens?
$0.28 input and $0.42 output on the official API, with cached input at $0.028. Third-party hosts range from $0.20 to $0.57 per million input tokens.
Who is the cheapest DeepSeek V3.2 provider?
By list price, GMI at $0.20 input and $0.32 output. For workloads with significant prompt caching, the official API's $0.028 cached-input rate usually produces the lowest real bill.
Is the cheapest V3.2 endpoint the same model?
Not always. Some budget endpoints serve FP8-quantized versions, and some cap context at 131K instead of 163K. Confirm precision and context before comparing prices.
Why does V3.2 pricing vary so much between providers?
Different hardware costs, utilization, quantization choices, and margin strategies, with no market mechanism forcing convergence. The full breakdown is in Why Token Prices Differ.
What is a blended price?
Your weighted cost across input, output, and cached tokens in the proportions your workload actually uses. It is the only number that predicts your bill, and the basis of the Token Index's Standard Blended Price.
Methodology
Provider pricing compiled from published rate cards and public pricing trackers, 2026. Workload examples use stated token volumes and cache assumptions; recompute with your own mix before choosing a provider. Blended pricing methodology follows the Mercatus Token Index Standard Blended Price. Last verified: 2026-08-07.