Background

DeepSeek opened both V4 Pro and V4 Flash weights under the MIT license and brought them onto its own API on April 24, 2026. From that day the release was framed in two stages: a long public preview, then a separate GA. Flash went official on July 31. Pro kept the preview label. Four months later, the Pro endpoint also goes official, under build number 0813.

V4 Pro is not a routine dense upgrade. It is a Mixture-of-Experts with 1.6T total parameters and 49B active per token. The architecture wires two new attention variants into the same model: DeepSeek calls them Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). The vendor's own ledger says that, at the 1M-token setting, single-token inference compute drops to 27 percent of what V3.2 needed, and KV cache drops to 10 percent. Both V4 models were pre-trained on more than 32T tokens; post-training grew domain-specific experts separately and then consolidated them back into one model through on-policy distillation.

The preview repo on Hugging Face has logged more than 1.4M downloads in the last month. The model card recommends a context of at least 384K tokens when running the model at maximum reasoning effort locally.

What ships in 0813

1) API economics carry over from preview

  • Input: $0.435 per million tokens on cache miss, $0.003625 per million tokens on cache hit
  • Output: $0.87 per million tokens
  • Context window: 1,048,576 tokens (1M), max output 384,000 tokens
  • Pro endpoint concurrency cap: 500; Flash endpoint: 2,500 — Pro remains the slow-but-heavy line

On OpenRouter, the deepseek-v4-pro-0813 page is stamped "Released Aug 12, 2026," matching the deepseek-v4-pro version string in DeepSeek's own API documentation.

2) Three operating modes

DeepSeek splits the API into three effort levels:

  • non-thinking
  • high reasoning effort
  • max effort (the docs describe it as "pushing the boundary of model reasoning capability")

The API is OpenAI ChatCompletions-compatible, Anthropic Messages-compatible, and also exposed through DeepSeek's own Responses API. Both Pro and Flash support tool calling and JSON outputs.

3) Vendor-reported benchmarks (V4-Pro-Max mode)

  • SWE-bench Verified: 80.6% resolved
  • Terminal Bench 2.0: 67.9% accuracy
  • GPQA Diamond: 90.1% pass@1
  • Humanity's Last Exam: 37.7% pass@1
  • MMLU-Pro: 87.5%
  • LiveCodeBench: 93.5% pass@1
  • Codeforces rating: 3,206
  • MRCR @ 1M tokens: 83.5 MMR

The card's own comparison table places V4-Pro-Max behind GPT-5.4 xHigh on Terminal Bench 2.0 (67.9 vs. 75.1) and behind Gemini-3.1-Pro on Humanity's Last Exam (37.7 vs. 44.4). On SWE-bench Verified (80.6) it lands level with Gemini-3.1-Pro, a hair behind Claude Opus 4.6 (80.8). It takes the top LiveCodeBench and Apex Shortlist scores in the table. None of these numbers has yet been independently replicated for the 0813 build.

4) Commercial side: Pro pricing will rise

DeepSeek's pricing page carries a notice that "a significant increase" in overall API pricing is coming "in the near future," with specifics to follow in an official announcement. Until that lands, the August 12 list prices hold. On OpenRouter, the average price users actually pay is well below the $0.435 list price, which the platform attributes to caching and discounts.

5) Open weights for 0813 are not out

The Hugging Face repositories still host the April preview builds, and the model card's download table points at those artifacts. DeepSeek has not announced a timeline for publishing 0813 weights, nor confirmed whether the GA build differs from preview beyond post-training. The V4 cadence the company has stated is clear: API first, weights later.

What it means

V4 Pro's GA is not a technical surprise. It is DeepSeek publicly setting a product position.

First, API-first, weights-later has been DeepSeek's stable release pattern since V3. Stripping the "preview" label merely locks down the commercial parameters — price, concurrency, effort levels — while weights trickle out in batches. It is a textbook "claim the slot first, let the community do the porting and benchmark replication second" play.

Second, the CSA + HCA pairing and the 27% / 10% compute and KV ledger are what pull "long context" back into the MoE camp. In the V3.2 era, KV growth in MoE made anything beyond roughly 100K tokens a non-starter for many teams. V4 Pro uses structural sparsity plus heavy compression to add a "1M tokens still runs" secondary track on top of the MoE mainline. Paired with OpenAI and Anthropic pushing GPT-5.6 and the Claude Opus line to comparable 1M context in the same window, the takeaway is: in the second half of 2026, 1M tokens is an entry ticket for frontier models, not a differentiator.

Third, the "significant price increase" notice signals that, with a 500-billion-yuan round reportedly in motion, DeepSeek is shifting from "win market share on price" to "sell API on capability." With V4 Flash 0731 already landing within a point of GPT-5.6 Luna on the Artificial Analysis Intelligence Index, the room for Pro to raise prices comes from V4-Pro-Max's positioning on developer workflows — SWE-bench and LiveCodeBench — where it has caught or passed GPT-5.4 and Claude Opus 4.6. Developer workloads tolerate higher per-token prices far better than chat or summarization do.

The question worth holding open: as long as the 0813 weights stay locked, DeepSeek is using the wording gap between preview and GA as a commercial buffer. The moment weights and GA land together, the next round of MoE long-context competition gets picked up immediately by Kimi, Qwen, and ByteDance's open-weight flagships. That window is roughly the next 30 days.

Sources