DeepSeek V4 Pro 0813 is a frontier-tier language model from Baseten. DeepSeek V4 Pro 0813 is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Baseten's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. DeepSeek is a Chinese AI lab's open-weights model family, known for competitive performance at very low API prices — particularly strong on coding and math benchmarks.
DeepSeek V4 Pro 0813 is priced at $1.32/1M input tokens and $3.96/1M output tokens. Output tokens are 3× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, DeepSeek V4 Pro 0813 costs approximately $3.30 per 1,000 API calls. Baseten does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $1.32/1M input rate.
Its 128K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation.
DeepSeek V4 Pro 0813 is best suited to reasoning, coding, chat. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how DeepSeek V4 Pro 0813 compares to similar models across these dimensions.
DeepSeek V4 Pro 0813 sits in the frontier tier — the highest-capability models available, used when quality is the primary constraint and cost is secondary. As an open-weights model, DeepSeek V4 Pro 0813 can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing DeepSeek V4 Pro 0813 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (N/A/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.
Input price vs 135 frontier models tracked. Avg: $5.69/1M.
| Model ID | DeepSeek V4 Pro 0813 |
| Provider | Baseten |
| Model Family | DeepSeek V4 |
| Tier | Frontier |
| Context Window | 128,000 tokens (128K) |
| Max Output Tokens | — |
| Input Price | $1.32 / 1M tokens |
| Output Price | $3.96 / 1M tokens |
| Cached Input Price | Not available |
| Throughput | — |
| Latency (P50) | — |
| Intelligence Score | — |
| LiveBench Score | — |
| Cost per Task (LiveBench) | — |
| Data Confidence | — |
| HLE Score | — |
| GPQA Diamond | — |
| SWE-bench Score | — |
| OSWorld Score | — |
| BrowseComp Score | — |
| Terminal-Bench Score | — |
| AutoBench Score | — |
| Parameters | — |
| Vision Support | No |
| Function Calling | Yes |
| Streaming | Yes |
| Reasoning Model | Yes |
| Open Weights | Yes |
| Data Source | Static (verified) |
| Last Updated | 2026-10-18 |
Self-hosting DeepSeek V4 Pro 0813? These GPUs are well-matched for this model's compute and memory requirements.