For AI developers and engineering teams tracking DeepSeek V4 GA, peak-valley pricing, and API migration: on July 20, 2026, DeepSeek V4 reached general availability — Agent capability, math reasoning, and code generation upgraded from the April preview, with peak-valley differentiated billing for the first time. This guide covers the preview-to-GA timeline, V4-Pro / V4-Flash architecture (CSA + HCA / mHC / Muon), SWE-bench Verified 80.6% and full benchmarks, comparison vs GPT-5.6 Sol and Claude Fable 5, complete peak-valley pricing tables and cost-saving tips, and the July 24 API migration deadline with six FAQs. Also read our DeepSeek V4 local inference guide and Kimi K3 review.
01

What Is DeepSeek V4 GA? Timeline From Preview to General Availability

After three months of preview testing, DeepSeek V4 GA (general availability) went live on July 20, 2026. Compared with the April preview, the GA release upgrades Agent performance, math reasoning, and code generation — and introduces peak-valley pricing for the first time, signaling DeepSeek's shift from near-free access toward disciplined commercial operations.

DateMilestone
April 24, 2026V4 preview + MIT open source: V4-Pro (1.6T) and V4-Flash (284B)
May 2026Production-tuned V4-Flash and V4-Pro; API generally available
June 2026V4-Pro output price permanently cut 75% (to $0.87/M tokens)
June 29, 2026Email to all API users: GA mid-July, first disclosure of peak-valley billing
July 19, 2026Selected developers receive GA gray-release access; media reports "full version imminent"
July 20, 2026GA release (publication date of this article)
July 24, 2026Legacy names deepseek-chat and deepseek-reasoner permanently retired

One-line summary: The preview was already strong; GA adds targeted Agent, math, and code upgrades plus a formal commercial billing structure. The underlying MoE architecture is unchanged — GA focuses on stability, optimization, and pricing maturity.

01

Missing the migration deadline: Code still calling deepseek-chat will break after July 24, 23:59 Beijing time.

02

Peak-hour blind calls: Weekday 09:00–12:00 and 14:00–18:00 Beijing time doubles rates — batch jobs without off-peak scheduling inflate bills fast.

03

No Pro/Flash routing: Using V4-Pro for simple routing wastes spend that V4-Flash (output $0.28/M off-peak) could handle.

04

Ignoring cache hits: Without Prompt Cache on repeated system prompts, you miss Flash cached input at just $0.0028/M.

05

Benchmark misread: SWE-bench Verified 80.6% is the open-source high, but Claude Fable 5 leads SWE-bench Pro (80.3%) — do not pick production models from a single leaderboard.

02

How Do V4-Pro and V4-Flash Differ? CSA, HCA, mHC, and Muon Explained

DeepSeek V4 delivers three architectural breakthroughs that cut KV-cache memory to 10% of V3.2 at 1M-token context (V4-Flash as low as 7%), making ultra-long context economically viable.

SpecV4-ProV4-Flash
Total parameters1.6 trillion (1.6T)284 billion (284B)
Active params per token49 billion (49B)13 billion (13B)
Transformer layers6143
Context window1,000,000 tokens1,000,000 tokens
Max output384K tokens384K tokens
PrecisionFP4 (expert weights) + FP8 (rest)FP4 + FP8 mixed
Pretraining data33T+ tokens32T+ tokens
LicenseMITMIT

Hybrid attention: CSA + HCA

DeepSeek V4 replaces V2/V3 MLA with a hybrid of two new attention mechanisms:

CSA

Compressed Sparse Attention: KV sequences are Softmax-gated and pooled 4x; an FP4 "lightning indexer" selects top-k sparse keys (Pro: top-1024, Flash: top-512) with a 128-token sliding window. At 1M context, inference FLOPs are just 27% of V3.2.

HCA

Heavily Compressed Attention: Tokens are compressed 128x then processed with global dense attention for long-range dependencies, complementing CSA. V4-Flash uses HCA in the first 2 layers then alternates CSA/HCA; V4-Pro follows a similar pattern.

Manifold-constrained hyper-connections (mHC) and Muon optimizer

mHC upgrades standard residual connections with a 4-channel residual stream and mixing matrices constrained to the Birkhoff polytope (doubly stochastic), keeping signals stable across 61 layers. The Muon optimizer (replacing AdamW) applies Newton-Schulz orthogonalization to gradients for faster convergence and more stable training.

Reasoning modeBehaviorBest for
Non-thinkNo chain-of-thought; fastest responseSimple Q&A, routing
Think HighExplicit reasoning (thinking tags)Medium complexity, code debugging
Think MaxMaximum reasoning; needs 384K+ contextHard math, long-chain Agents

Official sampling defaults: temperature=1.0, top_p=1.0 across all modes.

03

What Do the Benchmarks Show? SWE-bench, Cost-Performance, and Model Comparison

BenchmarkDeepSeek V4-ProClaude Fable 5GPT-5.6 UltraClaude Opus 4.8
SWE-bench Verified80.6% (open-source #1)96.0%Not separately published~69%
SWE-bench Pro55.4%80.3%78.1%69.2%
LiveCodeBench (Pass@1)93.5%88.1%87.4%83.2%
Codeforces Elo3,206
Terminal-Bench 2.183.9%88.0%85.1%82.7%

SWE-bench Verified tests real GitHub bug fixes. 80.6% is the current open-source high, tied with Gemini 3.1 Pro. SWE-bench Pro is stricter — Claude Fable 5's 80.3% still leads there.

Artificial Analysis: Claude Fable 5 costs $3.48 per Strategy & Ops index task (50 points); DeepSeek V4-Pro costs $0.03 for a comparable task (38 points) — 116x cheaper with roughly a 12-point score gap.

DimensionDeepSeek V4-ProGPT-5.6 SolClaude Fable 5
Open source / self-hostableYes (MIT)No (closed)No (closed)
Context window1M tokensNot publicly disclosed1M tokens
API output (off-peak)$0.87/M~$15/M$50/M
Peak output price$1.74/M
Code capabilityVery strong (near Fable 5)Very strongStrongest (SWE-bench Pro)
Data privacyYes (private deploy)NoNo

Scenario picks: Tight budget / high volume / private deploy → V4-Pro or V4-Flash; maximum code quality, cost secondary → Claude Fable 5; hard algorithms and math → GPT-5.6 Sol / Ultra; massive log or document ingestion → V4-Flash (cached input from $0.0028/M).

04

How Does Peak-Valley Pricing Work? Migration Steps Before July 24

The biggest GA billing change is peak-valley differentiated pricing, similar to time-of-use electricity rates. Peak hours (Beijing time): weekdays 09:00–12:00 and 14:00–18:00 — rates double.

ModelLine itemOff-peakPeak
V4-ProInput (cache hit)¥0.025 / $0.0035 / 1M2x
V4-ProInput (cache miss)¥3.00 / $0.435 / 1M¥6.00 / $0.87
V4-ProOutput¥6.00 / $0.87 / 1M¥12.00 / $1.74
V4-FlashInput (cache hit)¥0.02 / $0.0028 / 1M2x
V4-FlashInput (cache miss)¥1.00 / $0.14 / 1M¥2.00 / $0.28
V4-FlashOutput¥2.00 / $0.28 / 1M¥4.00 / $0.56

Four cost-saving tips: (1) Schedule non-real-time jobs off-peak (after 18:00 or before 09:00 Beijing time); (2) Build Prompt Cache for repeated system prefixes; (3) Route lightweight work to Flash, escalate hard reasoning to Pro; (4) Watch DeepSeek email alerts 24 hours before billing changes. Even at peak, V4-Pro output at $1.74/M remains 8.6x cheaper than Claude Opus 4.8 ($15/M).

Critical deadline: Legacy names deepseek-chat and deepseek-reasoner stop working July 24, 2026 at 15:59 UTC (July 24, 23:59 Beijing time).

Legacy modelNew modelNotes
deepseek-chatdeepseek-v4-flash (non-think)Fast; lightweight tasks
deepseek-reasonerdeepseek-v4-flash (think mode)Or upgrade to deepseek-v4-pro

Six migration steps:

01

Search the codebase: Find every reference to deepseek-chat and deepseek-reasoner.

02

Apply the mapping: Light chat → deepseek-v4-flash; former reasoner → Flash think mode or deepseek-v4-pro.

03

Update OpenAI SDK calls: Change only the model parameter; keep base_url at https://api.deepseek.com.

04

Enable thinking (optional): Use extra_body={"thinking": {"type": "enabled", "budget_tokens": 8000}} to replace reasoner behavior.

05

Anthropic SDK compatibility: V4 supports both OpenAI ChatCompletions and Anthropic Messages formats — update model only.

06

Staging validation: Run full regression in test before July 24; confirm zero legacy model names remain.

python
client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[...],
    extra_body={"thinking": {"type": "enabled", "budget_tokens": 8000}}
)
05

Is DeepSeek V4 GA Worth Upgrading? Citeable Data and Summary

DeepSeek V4 GA is one of 2026's most important open-model milestones. Its value is not beating Claude Fable 5 or GPT-5.6 on every chart today — it is delivering undisputed open-source peak performance at roughly 1/10 to 1/100 of closed flagship cost.

A

80.6% / open-source #1: SWE-bench Verified open-source high, tied with Gemini 3.1 Pro.

B

10% / 7%: At 1M tokens, KV-cache memory is just 10% of V3.2 (Flash as low as 7%).

C

$0.03 / 116x: V4-Pro Strategy & Ops task cost $0.03 vs Fable 5 at $3.48 — 116x price gap for ~24% score difference.

For teams that cannot send data offshore, indie developers on tight budgets, Agent engineers needing 1M context, and AI products chasing cost efficiency, DeepSeek V4 Pro remains the most balanced choice. Peak-valley billing adds planning overhead but stays highly competitive — a maturity signal, not a retreat from value.

Lay out the alternatives: running DeepSeek Agent load tests on a personal Mac hits sleep and network interruptions on long-context jobs; closed APIs alone cannot match V4's MIT license plus $0.87/M off-peak output on cost and data sovereignty; missing the July 24 migration cutoff causes hard production outages. For iOS CI/CD, 24/7 DeepSeek V4 Agent automation, and local ds4 inference validation, KVMNODE dedicated Mac Mini M4 cloud rental is usually the better host: Apple Silicon unified memory, open sudo, flexible daily/weekly/monthly terms. See the pricing page, help center, or order directly.

Data as of: July 20, 2026 · Sources: DeepSeek official docs, arXiv:2606.19348, Hugging Face, Artificial Analysis