DeepSeek V4 Release: What the Model Name Leak, the Preview Launch, and the Pre-GA Chatter Actually Tell Us

Nine days ago, two strings showed up on X: deepseek-v4-pro-202606 and deepseek-v4-flash-202605. Not a system card, not a WeChat post from Liang Wenfeng, not a press release — two lines in a screenshot that suggested a June-dated V4 Pro build and a May-dated V4 Flash build were on deck. That was July 4. As of today, DeepSeek V4 GA still has no confirmed date, but the surface area of what is public keeps expanding.

TLDR DeepSeek shipped a V4 Preview on April 24, 2026 with two open-weight models under MIT — V4-Pro (1.6T total, 49B active) and V4-Flash (284B total, 13B active) — both at a 1M-token default context using a new Compressed Sparse Attention plus Heavily Compressed Attention design. Two months later, leaked model IDs and community chatter suggest a full GA is close, likely with revised pricing. The advertised gains are real on paper (SWE-bench Verified 80.6% for Pro), the serving cost story is the most-cited claim (roughly 10× reduction versus V3.2), and the reception in a crowded field has been noticeably quieter than R1's. Several architectural claims that circulated pre-preview — most prominently Engram conditional memory and Manifold-Constrained Hyper-Connections — do not appear in the official V4 Preview announcement.

Key Takeaways

• Two model IDs — deepseek-v4-pro-202606 and deepseek-v4-flash-202605 — leaked via @teortaxesTex on July 4, 2026, pointing to an imminent GA build.
• The April 24 V4 Preview is already live: V4-Pro at 1.6T/49B active, V4-Flash at 284B/13B active, both 1M context, MIT-licensed.
• Attention rework combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), targeting serving cost, not raw capability.
• V4-Pro is reported at 80.6% on SWE-bench Verified — within 0.2 points of Claude Opus 4.6 (80.8%), at roughly 1/7 the output price.
• Reception has been muted compared to R1: Kimi K2.6 outscored V4 on several public evals; Artificial Analysis has V4 Pro second to K2.6.
• The Engram memory architecture that dominated pre-launch coverage is not named in the official preview post — treat it as unverified for V4.

What Was Actually Seen

Two threads of evidence sit under the current V4 discussion, and it's worth keeping them separate.

The first is the April 24 V4 Preview itself. Per the official DeepSeek API changelog, DeepSeek shipped two models — deepseek-v4-pro and deepseek-v4-flash — with weights on HuggingFace under MIT, a tech report on the V4-Pro repo, and API access via both the OpenAI ChatCompletions and Anthropic-compatible endpoints. The advertised specs: V4-Pro at 1.6T total parameters with 49B activated, V4-Flash at 284B total with 13B activated, both defaulting to a 1M-token context window, both supporting dual Thinking / Non-Thinking modes. The changelog also confirms the old deepseek-chat and deepseek-reasoner aliases will be fully retired on July 24, 2026 — routing to deepseek-v4-flash in the interim.

The second thread is the pre-GA signal from the last ten days. On July 4, @teortaxesTex posted the two dated model IDs: deepseek-v4-pro-202606 and deepseek-v4-flash-202605. The naming convention matches DeepSeek's usual pattern (model name + YYYYMM build date), and the fact that both post-date the April 24 preview suggests a newer, GA-candidate build. Two days later, the same author predicted a "DeepSeek trifecta" within two weeks that includes "literally DeepSeek V4." On July 13, @LinearUncle wrote in Chinese that "the V4 official release is coming out in the next few days," and expects a price increase to accompany it. @oran_ge separately mentioned V4 GA in a broader rumor roundup positioning V4 against GLM 5.2.

Source: @teortaxesTex

There's also a Reddit thread worth flagging. On r/DeepSeek, a user reports that the V4-Pro currently exposed on the official API "is definitely a newer improved version" than the April preview, with more concise outputs across multiple turns. A commenter cites Bilibili uploaders who believe they were whitelisted for a gray-release test, generating HTML-based one-shot game clones from a single prompt. None of this is confirmed by DeepSeek, and neither the Reddit thread nor the Bilibili claims include controlled benchmark runs.

Architecture: The Serving-Cost Story

The most defensible read on V4's technical direction is that this is a serving-cost release, not a capability release. That framing is Lambda's take in their V4 write-up, and it matches what the tech report emphasizes.

Two named components anchor the attention rework:

• Compressed Sparse Attention (CSA) — the base sparse mechanism V4 uses to cut per-token compute at long context.
• Heavily Compressed Attention (HCA) — a further-compressed variant that stacks with CSA to cut KV-cache memory footprint.

Together, per Lambda, this hybrid drops cost-to-serve by roughly 10× versus V3.2, with about 10× less memory at the same context length. Lightning AI's comparison puts KV cache at 10% of V3.2 and inference FLOPs at 27% of V3.2 for a single-token pass. That is the mechanism enabling the 1M default context. It is also, notably, a mechanism aimed squarely at inference economics rather than headline evals — the tech report's own long-context benchmark gains are, in Lambda's phrasing, "inconsistent."

The training side got less attention. DeepSeek confirms the models were "seamlessly integrated with leading AI agents like Claude Code, OpenClaw & OpenCode" and that V4 already drives their in-house agentic coding. The preview note also references "Token-wise compression + DSA (DeepSeek Sparse Attention)" as the novel attention family, which is the through-line from V3.2-Exp.

Coined Terminology You'll See Cited

Pre-launch coverage introduced several capitalized terms that have started showing up in downstream write-ups. It's worth being precise about which are in the official preview and which are not:

• Compressed Sparse Attention (CSA) — the base sparse attention layer, referenced in the tech report and Lightning AI's write-up.
• Heavily Compressed Attention (HCA) — the stacked-compression variant that reduces KV cache footprint.
• DeepSeek Sparse Attention (DSA) — the V3.2-Exp lineage that CSA/HCA extend.
• Expert Mode / Instant Mode — the two user-facing modes on chat.deepseek.com, per the API changelog.
• Thinking / Non-Thinking Mode — the dual API modes both V4 models support.
• Engram Conditional Memory — a DeepSeek research concept covered heavily by a Medium leak write-up and referenced in the pre-launch Reddit summary. It does NOT appear by name in the official V4 Preview announcement. Treat it as unverified for V4.
• Manifold-Constrained Hyper-Connections (mHC) — a rumored training-stability technique cited by Atlas Cloud. Same caveat: not named in the official preview.

Two of these — CSA and HCA — are the ones to cite. They are the mechanically load-bearing acronyms in the actual release.

The Numbers That Are on the Record

Numbers first, caveats after. Everything below traces to either the DeepSeek API changelog, the tech report as summarized by Lightning AI, or Lambda's serving analysis.

Model sizing. V4-Pro: 1.6T total parameters, 49B activated. V4-Flash: 284B total, 13B activated. V3.2 baseline was 671B-685B total, ~37B activated.

Context window. 1M tokens default across V4 services. V3.2 shipped at 128K.

Serving efficiency (V4-Pro vs V3.2 at 1M context). KV cache: 10% of V3.2. Inference FLOPs per token: 27% of V3.2. Cost-to-serve: ~10× cheaper.

Pricing on preview (per 1M tokens). V4-Pro: $1.74 input cache miss, $0.145 input cache hit, $3.48 output. V4-Flash: $0.14 input cache miss, $0.028 input cache hit, $0.28 output. Claude Opus 4.6 for comparison: $5.00 input, $25.00 output.

Benchmark scores from the tech report (as cited by Lightning AI). SWE-bench Verified: V4-Pro 80.6%, V4-Flash 79.0%, V3.2 ~69%, Claude Opus 4.6 80.8%. LiveCodeBench: V4-Pro 93.5%, V4-Flash 91.6%. Codeforces rating: V4-Pro 3206, V4-Flash 3052. Terminal-Bench 2.0: V4-Pro 67.9%, V4-Flash 56.9%. GPQA Diamond: V4-Pro 90.1%, V4-Flash 88.1%.

Speed. ~33 tokens/sec for V4-Pro, ~60+ tokens/sec for V4-Flash, per Lightning AI's measurements.

That is the corpus of numbers that has a paper trail. Anything not in this list — including the 30% VRAM reduction, 50% long-context compute savings, and $0.27/M pricing figures that circulated pre-launch — traces to leak coverage, not the shipped preview.

Why This Matters for Builders

The V4 Preview's real deliverable is a per-token economics reset on the open-weight side. At $3.48 per 1M output tokens on V4-Pro versus $25 on Claude Opus 4.6, the gap on advertised coding evals is 0.2 points on SWE-bench Verified. If that ratio holds on private evals — a large "if" — the calculus for teams running high-volume agentic pipelines shifts, particularly at long context where the CSA/HCA compression pays off structurally rather than as a marketing figure.

At roughly one-seventh the output price of Claude Opus 4.6 for a 0.2-point gap on SWE-bench Verified, DeepSeek V4-Pro reframes what "frontier-adjacent" costs to serve.

The caveat is that the field V4 shipped into is unusually crowded. Lambda's write-up notes that Kimi K2.6 has outscored V4 on several public coding evals and ranked higher on Artificial Analysis's Intelligence Index. GLM 5.2 landed near the same window. GPT-5.6 was already out. When R1 dropped in January 2025, it landed in a quieter release calendar; V4 landed in a busier one, and the reception reflected that. Lambda's framing — "the most expected open-source model ever released, and the quietest landing" — is defensible on the evidence.

For engineering decisions today, the load-bearing question is whether V4-Pro's advertised long-context economics hold under your workload. The tech report's 10× serving-cost improvement is a statement about attention mechanism costs, not end-to-end system costs. Anyone planning to move a 1M-context agent pipeline to V4 should measure their own KV cache and throughput before committing.

DeepSeek V4 vs Claude Opus 4.6: What the Signal Says

Both Lightning AI and Lambda draw the V4-vs-Opus-4.6 comparison directly, and it is the most cited framing in the current wave of coverage. Here is what the signal set actually supports.

• Context window. V4-Pro: 1M tokens. Claude Opus 4.6: 1M tokens. Parity, per Lightning AI's table.
• SWE-bench Verified. V4-Pro: 80.6%. Claude Opus 4.6: 80.8%. A 0.2-point gap, per the DeepSeek tech report as summarized by Lightning AI.
• Output pricing per 1M tokens. V4-Pro: $3.48. Claude Opus 4.6: $25.00. A ~7.2× gap.
• LiveCodeBench. V4-Pro: 93.5%. Claude Opus 4.6: 88.8%. V4 ahead by 4.7 points.
• GPQA Diamond. V4-Pro: 90.1%. Claude Opus 4.6: 91.3%. Claude ahead by 1.2 points.
• Open weights. V4-Pro: MIT-licensed. Claude Opus 4.6: closed.

This is a comparison table drawn from one source's summary of the vendor's own tech report. Every serious builder should treat it as directional. Independent replication on private evals — Kilo Code, Artificial Analysis, and the practitioner runs Lambda cites — is what will matter over the next few weeks. Ari Messer at Kilo flagged this ahead of launch: pre-release leaks tend to overweight benchmark deltas and underweight the "does it hold up in a real repo" question.

What We Know vs. What We Don't

The signal set supports a lot of narrow claims and very few sweeping ones. Splitting them cleanly:

What we know (from primary or well-sourced material):

• Two model IDs, deepseek-v4-pro-202606 and deepseek-v4-flash-202605, were surfaced on X by @teortaxesTex on July 4, 2026.
• DeepSeek shipped a V4 Preview on April 24, 2026, with DeepSeek-V4-Pro and DeepSeek-V4-Flash exposed via the API and open weights on HuggingFace.
• DeepSeek-V4-Pro is advertised at 1.6T total parameters with 49B activated, and DeepSeek-V4-Flash at 284B total with 13B activated.
• DeepSeek V4 defaults to a 1M-token context window across official services.
• The V4 preview introduces a hybrid attention design combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), extending DSA.
• DeepSeek V4-Pro scores 80.6% on SWE-bench Verified and V4-Flash scores 79.0%, per the tech report summarized by Lightning AI.
• Both DeepSeek-V4-Pro and DeepSeek-V4-Flash open weights are released under the MIT license.

What we don't know (open questions the signal set cannot resolve):

• DeepSeek V4 GA has not been dated by the company. Community observers including @teortaxesTex and @LinearUncle expect it within days to two weeks of mid-July 2026, but this timing is unverified.
• The Engram conditional memory concept appears in a DeepSeek-authored arXiv paper referenced by community write-ups, but the official V4 Preview announcement does not name Engram as a shipped component.
• Unverified: @LinearUncle notes prices are expected to rise at GA, but DeepSeek has not published GA pricing at time of writing.
• A Reddit thread on r/DeepSeek claims the live V4 Pro on the official API has silently improved, with references to a Bilibili gray-release test, but neither DeepSeek nor independent evaluators have confirmed this.
• The tech report's serving-cost claims (~10× cheaper vs V3.2) have not been independently reproduced across diverse production workloads.
• Long-context benchmark performance is described by Lambda as "inconsistent"; specific pass rates at 500K+ context have not been published.

How to Evaluate DeepSeek V4 Yourself

Anyone considering a V4 migration should approach the numbers as hypotheses to test, not as commitments. Three concrete evaluation moves are worth doing before writing production glue code.

First, run a private SWE-bench Verified subset against V4-Pro and your current model on the same harness, on the same 50-issue slice. The 80.6% headline is a directional signal; whether it holds on the 15% of your repo that agents actually touch is the real question. If V4 clears the gap on your slice, that's meaningful. If it lands 5 points behind on your subset while the headline shows parity, the eval is telling you something about training distribution.

Second, measure serving cost end-to-end at your actual context lengths. The 10× compression claim is at 1M tokens; at 32K, the delta looks different. Lambda's framing — "if 10x cheaper inference holds in production, the economics of running 1M-context models change" — is a conditional statement, and the condition is exactly what you need to verify on your workload.

Third, stress-test the Thinking / Non-Thinking mode boundaries. Both modes on both models are new API surface. The retirement of deepseek-chat and deepseek-reasoner on July 24 means any legacy integration will start hitting V4-Flash automatically, and behavior differences between the old reasoner and the new Thinking mode are worth pinning down before they surprise a production caller.

The Week Ahead

Three things to watch specifically:

• An official DeepSeek announcement of the GA build. The -202606 and -202605 build tags suggest a build already exists internally. Watch for the API changelog to add a new entry, likely on a Tuesday per DeepSeek's usual pattern.
• Third-party Artificial Analysis and Kilo Code eval refreshes. The current standings — Kimi K2.6 ahead on the AA Intelligence Index — will move once independent evaluators re-run against a GA build.
• A pricing update. @LinearUncle's expectation of a price hike is one to watch specifically because DeepSeek has historically raised prices in narrow bands and telegraphed them via the API docs page.

Building similar long-context, cost-conscious agentic coding pipelines? On kie.ai you can try Claude Opus 4.7, GPT-5.6, and Gemini 3.1 Pro.