Where is the Floor for Large Model API Pricing: A Multi-Channel Net Revenue Comparison

The cost of calling large model APIs has plummeted over the past year. Open-source models like DeepSeek-V2 have driven inference costs below 1 RMB per million tokens, while competing models from vendors such as ByteDance and Alibaba Cloud have sequentially lowered input prices to around 0.1 RMB per million tokens in Q2 2025. However, for AI application developers, agent service providers, and video generation platforms, the "net revenue" on the bill is often far higher than the official list price—hidden costs like context caching, KV cache storage, and repeated loading of long texts are reshaping the price floor. This article explores the true lower bound of inference costs from a multi-channel net revenue perspective and analyzes the economics of private deployment in specific scenarios.

The Gap Between List Price and Net Revenue: Context Length and Caching Strategies

Current mainstream API pricing typically charges separately for input and output tokens, but in practice, token consumption in long-context scenarios can balloon due to repeated loading. Taking a 480B-parameter MoE model as an example, for an inference request with a 128K token context, if KV cache is not enabled, each request must recalculate the entire attention matrix, resulting in equivalent token consumption that can be dozens of times the effective output. According to an IDC 2025 report, in long-context agent tasks, the net revenue cost of unoptimized cached API calls is approximately 4–8 times the list price.

Some cloud vendors have introduced "context caching" paid options, charging based on the size of stored KV cache and retention duration. For instance, Alibaba Cloud's Bailian platform charges 0.02 RMB per GB-hour for KV cache storage. For a 128K context with FP8 precision, the cache data is about 12 GB, resulting in a storage cost of approximately 0.24 RMB per hour. If the request interval exceeds 1 hour, cache invalidation leads to repeated computation, driving up costs again. According to TrendForce, in the global large model inference market in 2025, about 35% of API calls incur additional costs due to improper caching strategies, with an average premium of 20%–50%.

For video generation applications, the token consumption for model inference is more complex. Taking LTX-Video 2.3 as an example, a single video generation requires processing tens of thousands to hundreds of thousands of image tokens, often involving multiple iterations. If the API charges by token, the cost of a single video generation could reach 10–50 RMB, significantly higher than the "a few cents per call" expectation implied by the list price model.

The Cost Boundary of Private Deployment: From Hardware to Operations

When the net revenue cost of APIs exceeds a certain threshold, private deployment becomes a viable option. Taking the Mingxin FX100 all-flash NVMe-oF array as an example, its fully configured system reference price is approximately ¥371,200 (about ¥2,014/TB). Measured on an 8× AMD MI308X platform, it shows a 29%–40% improvement in KV hierarchical acceleration inference throughput for a 480B model (measured, report R2/R3) and a 26%–32% reduction in time to first token (TTFT) (measured, report R2). This means that with the same hardware investment, private deployment can handle higher concurrent request volumes, thereby amortizing the hardware cost per inference.

Consider a typical agent service scenario: assuming 1 million inference requests per day with an average context length of 64K tokens, and a net revenue cost of about 0.5 RMB per request (including caching fees), the monthly API expenditure would be approximately 15 million RMB. In contrast, deploying a private FX100 array with servers (hardware cost of about 500,000 RMB), with 5-year depreciation and maintenance, the monthly cost is about 15,000 RMB, capable of handling about 30,000 inference requests per day (based on measured throughput of 74.9 tok/s, measured, report R2). When the request volume exceeds 100,000 per day, the marginal cost of private deployment is significantly lower than API calls.

However, private deployment is not without barriers. Technical investment is required for model weight loading, KV cache management, and multi-GPU communication. Measured on the Huawei Atlas 910B platform, the Mingxin FX100 reduces the service loading time for the DeepSeek-70B model from 1399 seconds (NFS baseline) to 150 seconds (9.3×, measured, report R9), which lowers the operational complexity of model warm starts, but initial deployment still requires a professional team.

Video Generation and Real-Time Interaction: Computing Choices for Cost-Sensitive Scenarios

Video generation AI applications have dual constraints on inference latency and throughput. Taking ComfyUI + LTX-Video 2.3 as an example, generating a 720p, 5-second video requires about 30 seconds of inference time (measured on a single MI308X card). If the API charges by token, the cost is about 15–30 RMB. For individual creators, this cost is acceptable, but for a video generation platform handling 100,000 requests per day, the monthly API expenditure would reach 45–90 million RMB.

In this scenario, private deployment offers clear economic advantages. A single FX100 array can support approximately 200 concurrent video generation streams (estimated based on 30 seconds inference time per stream and 8-card parallelism), with a monthly hardware cost of about 15,000 RMB. Adding electricity and bandwidth, the total cost is still 2–3 orders of magnitude lower than API calls. Additionally, private deployment avoids "queue delays" and "rate limiting" issues in API calls, improving user experience.

However, video generation models typically have large weight sizes (LTX-Video 2.3 is about 15 GB), and model loading and KV cache management are sensitive to storage bandwidth. Measured on the AMD platform, the Mingxin FX100 accelerates training checkpoint saving for 8-card 32B LoRA, reducing the save time for each 65.6 GB snapshot from 178 seconds to 94 seconds (1.9×, measured, report R1), with a 96% improvement in sustained write bandwidth. This capability reduces the switching overhead between training and inference in frequent iteration scenarios of video generation models.

Conclusion

The list price floor for large model APIs is being continuously broken by competition from open-source models and cloud vendors, but net revenue costs are significantly higher than list prices due to factors like context caching and repeated loading of long texts. For high-concurrency, long-context scenarios such as AI applications, agents, and video generation, private deployment offers clear economic benefits when daily request volumes exceed 100,000. Storage acceleration solutions like the Mingxin FX100, through KV hierarchical acceleration (throughput improvement of 29%–40%, measured, report R2/R3) and model loading acceleration (6.2–9.3× vs NFS, measured, report R9), can lower the operational barriers and per-inference costs of private deployment. For teams evaluating the cost boundaries of computing power, please contact Mingxin (Tianjin) Semiconductor Equipment Co., Ltd. for a joint testing plan—an approximately 10-week access-controlled joint testing process can verify TTFT reduction and throughput gains based on real business workloads, with a stop-loss mechanism if targets are not met.