DeepSeek V4 Pro Is GA: 1.6T Parameters, 1M Context, and a Price Hike Coming
DeepSeek released the production version of its flagship model this week. Build 0813 ended a preview window that ran nearly four months (Unite.AI). The company formally announced the release on August 13, 2026 (Reuters). DeepSeek models already power the AI chat features on this site. The family’s flagship is now a stable production target.
What changed
Section titled “What changed”Calls to the deepseek-v4-pro endpoint now route to the GA build automatically. The endpoint name did not change (AI/TLDR). DeepSeek says the new build “greatly enhances agent capabilities” and is available through the API, the app, and the web (Reuters). The build appeared on OpenRouter’s model page on August 12 (Unite.AI; OpenRouter). API users spotted the swap in the system_fingerprint field, which now returns fp_v4pro_20260812 (r/DeepSeek).
The spec block
Section titled “The spec block”| Spec | Value |
|---|---|
| Build | DeepSeek-V4-Pro-0813 (GA, August 12, 2026) |
| Architecture | Mixture of experts, 1.6 trillion total parameters |
| Active parameters per token | About 49 billion |
| Context window | 1 million tokens |
| Max output | 384,000 tokens |
| Attention | Compressed Sparse Attention + Heavily Compressed Attention |
| Thinking modes | non-think, think high, think max |
| Pretraining data | More than 32 trillion tokens |
Sources: GMICloud, Apidog, MindStudio.
The attention design cuts inference cost hard. DeepSeek reports the V4 line runs at 27% of the single-token compute and 10% of the KV cache of V3.2 (Apidog). Long-context workloads are the target, not the edge case.
The pricing math
Section titled “The pricing math”| Item | Price per million tokens |
|---|---|
| Input, cache miss | $0.435 |
| Input, cache hit | $0.003625 (about 120x less) |
| Output | $0.87 |
Caching is automatic. There is no parameter to enable it (AIHubMix). Teams that reuse long prefixes, like system prompts or document chunks, get the cache-hit rate on every repeated token (Apidog). Concurrency is capped at 500 requests for Pro, versus 2,500 for Flash (GMICloud).
The price holds for now. It will not hold for long. DeepSeek’s pricing page warns that “a significant increase” is coming soon (AI/TLDR). Reuters confirms DeepSeek will raise API pricing for V4 Pro and V4 Flash and introduce peak and off-peak pricing (Reuters).
How the rollout happened
Section titled “How the rollout happened”DeepSeek shipped the family in three steps.
- April 24, 2026: the V4 series previewed with open weights for Pro and Flash under the MIT license (Unite.AI).
- July 31, 2026: V4-Flash went official first. Flash runs about 300B total parameters with roughly 13B active per token, at $0.14 input and $0.28 output per million tokens (Apidog; Yotta Labs).
- August 12, 2026: the Pro flagship went GA as build 0813 (GMICloud).
The order was deliberate. When Flash went official, DeepSeek published agent-benchmark results showing the re-trained small model outscoring the preview Pro on internal coding-agent suites (Unite.AI). Flash became the default agent workload model. The 0813 build is the flagship’s answer.
Why it matters for teams
Section titled “Why it matters for teams”The V4 series is trained for agentic work: coding assistants, multi-step automation, and long-document synthesis (Unite.AI). The price gap to the frontier is the headline. V4 Pro runs about $0.435 in and $0.87 out per million tokens, against roughly $10.50 per million tokens for Gemini 3 Pro (MindStudio). That is a 50 to 60 times spread on the same class of agentic tasks (MindStudio).
Benchmarks back the value claim. Official scores put V4 Pro on top of Cybergym (83.3) and a terminal automation benchmark (31.8), with a modest gap to leaders on HLE and NL2Repo (MindStudio). Independent testing on a custom coding and agentic benchmark measured 76.25%, up from the preview’s 24.8% (MindStudio).
Integration is a one-line change for most stacks. The API is OpenAI-compatible, so the openai SDK works with model="deepseek-v4-pro" (Apidog). Anthropic Messages format and DeepSeek’s own Responses API also work (Apidog). Thinking modes return a reasoning_content field alongside the answer (Apidog).
One open item: weights. Hugging Face still hosts the April preview builds. DeepSeek has not announced a timeline for publishing the 0813 weights (Unite.AI). The stated cadence for the V4 line runs through the API first.
Rules to apply
Section titled “Rules to apply”- Track the build, not the endpoint name.
deepseek-v4-promoved from preview to GA with zero code change on your side. If you need reproducibility, log thesystem_fingerprintso you know which snapshot served a response. - Structure prompts for cache hits. Keep system prompts and document prefixes stable. The 120x cache gap decides whether this model is cheap or not (Apidog).
- Budget for the hike now. The increase is announced, with peak and off-peak pricing to follow (Reuters). Model your cost at 1.5x to 2x the current rate.
- Match the model to the load. Flash carries high-throughput traffic at 2,500 concurrent requests (GMICloud). Pro earns its 500-cap on long-context reasoning and heavy agent loops.
- Do not wait on self-hosting. The 0813 weights are not published. If you need open weights today, the April previews are what exists (Unite.AI).
This continues the cost story from our post on routing cloud AI instead of burning it. The economics just got sharper: a frontier-class agent model at cache-hit prices is now a default, not a bargain.