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DeepSeek's open-weight, MIT-licensed V4 Pro reasoning model — a large mixture-of-experts system with a million-token context window and selectable reasoning effort for long, multi-step tasks.
Overview. DeepSeek-V4-Pro-0813 is the general-availability release of DeepSeek's V4 Pro line, succeeding the V4-Pro Preview. It's a sparse mixture-of-experts model with roughly 1.6T total parameters and 49B active per token, released under an MIT license with open weights published on HuggingFace. Developers reach for it when they need a reasoning-focused model that can hold a full million-token context in a single call.
Key strengths. The model is built as a dedicated reasoning system, with selectable reasoning-effort levels (low, high, max) that expose visible thinking traces during generation. Its headline capacity numbers stand out: a 1,048,576-token context window paired with up to 384K output tokens, letting it work through very large inputs and long, multi-step outputs in one pass. On the Artificial Analysis Intelligence Index it scores 42 at max reasoning effort — good for #6 of 112 tracked models and well above the median for open-weight models of similar size (median: 22).
When to use it. Use DeepSeek-V4-Pro-0813 for long-document and long-codebase analysis, multi-step reasoning chains, or agentic workflows where the value of a huge context window and adjustable reasoning depth outweighs the need for instant responses. Its generation profile — extended thinking traces and large output budgets — fits batch and asynchronous pipelines better than latency-sensitive, interactive chat. Dial reasoning effort down to low for faster, lighter passes, or up to max when task complexity justifies the extra thinking time.
Limitations. The model is text-only: it has no vision, audio, or native document-file ingestion, so image- or speech-driven use cases need a different model. Its batch-oriented latency profile and extended thinking traces mean it isn't a fit for realtime, low-latency applications, and no training data cutoff date has been published for it. Available benchmark data is currently limited to a single aggregate Intelligence Index score, without confirmed per-domain (code, math, agentic, etc.) breakdowns.
Sovereignty note. DeepSeek-V4-Pro-0813 is released under an MIT license with open weights, so it can be self-hosted on infrastructure you control rather than relying solely on a third-party API. No data-residency or CLOUD Act exposure details for hosted endpoints of this model have been confirmed yet.
15 use cases classified by the curator. The note says why.
Massive context window plus tool-calling supports retrieval-augmented answer generation over very large document sets.
1,048,576-token context window allows ingestion of very large documents in a single pass, a clear architectural fit for document Q&A.
Native tool-calling capability directly supports structured function-calling application patterns.
Extremely large context window suits ingesting multiple full papers simultaneously for comparative literature review.
The 1M-token context window supports synthesizing across many long source documents without chunking, a key requirement for research synthesis.
1M-token context allows whole-document summarisation without truncation, a direct architectural advantage over smaller-context models.
Tool-calling support enables agentic coding loops, but the absence of published SWE-bench or coding benchmarks keeps this at acceptable rather than strong.
1M-token context supports reviewing large codebases in one pass, but no code-specific benchmark scores are published to justify a stronger verdict.
Large context window supports long-form output and revision cycles, though no curated writing-quality benchmarks are yet available.
Tool-calling plus a very large context window supports long planning chains, though no reasoning benchmarks are published yet to confirm strong-tier planning quality.
Large context window helps with in-context retrieval workflows, though this is a generative model rather than a dedicated embedding/retrieval system.
No vision/OCR capability is documented, making this unsuitable for document OCR tasks.
No vision capability is listed for this model, so it cannot support image-based Q&A.
Lacks vision capability entirely, ruling out screenshot understanding use cases.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-ai/deepseek-v4-pro-0813",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at