DeepSeek V3 vs. DeepSeek V4: Architecture, Benchmarks, and Pricing Compared (2026)

DeepSeek V4, released on April 24, 2026, ends more than a year of speculation. A year after V3 rattled the AI industry, DeepSeek's newest generation ships in two variants — V4-Pro and V4-Flash — and delivers on the efficiency promises the community had been anticipating.

This article compares DeepSeek V3 vs. DeepSeek V4 using confirmed architectural specifications, official benchmark data, and published API pricing. Every claim about V4 in this article reflects the released model, not roadmap projections.

DeepSeek V3: Confirmed Architecture and Specifications

DeepSeek V3 is a Mixture-of-Experts (MoE) model with 671 billion total parameters, activating 37 billion parameters per token during inference. This selective activation pattern lets the model match the performance of much larger dense models while keeping per-token compute tractable.

V3 Key Specifications

• Architecture: Mixture-of-Experts (MoE)
• Total parameters: 671 billion
• Activated parameters per token: 37 billion
• Context window: 128,000 tokens
• Training precision: FP8 mixed-precision
• Parallelism strategy: DualPipe pipeline parallelism
• Load balancing: Auxiliary-free mechanism (no routing loss overhead)
• Sequence prediction: Multi-Token Prediction (MTP) for faster training and fluent generation
• License: MIT (open weights)

V3 competed credibly with GPT-4o and Claude 3.5 Sonnet at launch and set the foundation for what V4 builds on. Its 128K context window and efficient MoE routing made it a practical production choice for complex coding and reasoning tasks.

DeepSeek V4: Two Variants for Different Workloads

DeepSeek V4 ships in two distinct models. Unlike V3's single flagship approach, the V4 generation splits into a high-capability model and a cost-optimized variant:

• V4-Pro: 1.6T parameter flagship — maximum reasoning and coding performance
• V4-Flash: 284B parameter efficient model — lower cost, faster throughput, same 1M context window

Both models release under the MIT license with open weights on Hugging Face. For a full breakdown of V4 features and API setup, see the DeepSeek V4 release breakdown and feature guide.

DeepSeek V4-Pro: Confirmed Specifications

• Architecture: Mixture-of-Experts (MoE)
• Total parameters: 1.6 trillion
• Activated parameters per token: 49 billion
• Context window: 1,000,000 tokens (1M)
• Max output tokens: 384,000
• Pre-training tokens: 32 trillion+
• Training precision: FP4 (MoE expert weights) + FP8 (other parameters)
• Attention: CSA + HCA hybrid (Compressed Sparse Attention + Heavily Compressed Attention)
• Residual connections: Manifold-Constrained Hyper-Connections (mHC)
• Optimizer: Muon optimizer
• License: MIT (open weights)

DeepSeek V4-Flash: Confirmed Specifications

• Architecture: Mixture-of-Experts (MoE)
• Total parameters: 284 billion
• Activated parameters per token: 13 billion
• Context window: 1,000,000 tokens (1M)
• Max output tokens: 384,000
• License: MIT (open weights)

V4 Architectural Innovations

CSA + HCA Hybrid Attention

V3 uses standard multi-head attention across its full context. V4-Pro replaces this with a two-tier hybrid: Compressed Sparse Attention (CSA) for local token relationships and Heavily Compressed Attention (HCA) for long-range dependencies with aggressive key-value compression.

The efficiency gain is substantial: in a 1M-token context, V4-Pro requires only 27% of the inference FLOPs and 10% of the KV cache that DeepSeek V3.2 requires at equivalent context lengths. The 1M context expansion does not come with a proportional compute cost increase.

Manifold-Constrained Hyper-Connections (mHC)

Standard residual connections add layer outputs directly to the input stream, which can cause representational collapse in very deep networks. V4's mHC constrains these connections to a learned manifold, improving gradient signal propagation across layers while preserving model expressivity. The result is more stable training at scale.

Muon Optimizer

V4 replaces AdamW with the Muon optimizer, which applies momentum updates in the orthogonal complement of the gradient direction. This reduces interference between parameter updates and improves convergence stability across the 32T+ token pre-training run — important at V4-Pro's scale where training instability is a real risk.

FP4 + FP8 Mixed Precision

V4-Pro's MoE expert weights use FP4 precision — a significant reduction from V3's FP8. Non-expert parameters retain FP8. This mixed approach cuts memory bandwidth requirements for the 1.6T parameter model without the numerical instability that pure FP4 training historically produced.

Benchmark Comparison: V3 vs V4-Pro vs V4-Flash

The following benchmarks include verified V3.2 scores from official DeepSeek reports and V4 scores from DeepSeek's technical release documentation and early independent evaluations. Independent community replication of V4 scores is ongoing.

V3.2 scores are from verified official evaluations. V4-Flash and V4-Pro scores marked with ~ are from DeepSeek's official technical report; independent community benchmarks are in progress.

V4-Pro's SWE-bench jump from 67.8% to ~81% is the standout result for software engineering workloads. That 13-point gain on real-world GitHub issue resolution reflects the combination of improved reasoning from the Muon optimizer and the CSA+HCA attention's ability to hold more context in active working memory.

Pricing Comparison: V3.2 vs V4-Flash vs V4-Pro

Pricing sourced from DeepSeek's official API documentation as of April 2026. Verify current rates at api-docs.deepseek.com/quick_start/pricing.

The most surprising pricing story is V4-Flash: it costs 50% less per input token than V3.2 and delivers a 1M token context window. For teams currently running V3.2 for long-context tasks, V4-Flash is a direct cost reduction with a capability upgrade.

V4-Pro's $1.74/$3.48 pricing is higher than V3.2, but remains a fraction of comparable closed-source frontier models. For context: GPT-5.5 and Claude Opus 4.7 output tokens typically run $15–$75 per million — making V4-Pro's output pricing roughly 4–20x cheaper for equivalent capability tiers.

For a full comparison of V4 against other frontier models in the current ecosystem, see DeepSeek V4 vs Qwen, GPT, Claude, Kimi, and MiniMax.

Context Window: 128K to 1M Tokens

The jump from V3's 128K to V4's 1M token context is the single most impactful practical change for developers building production applications. A 1M token window can hold:

• An entire medium-sized codebase (50,000–200,000 lines of code)
• Hundreds of pages of legal, financial, or technical documents in a single prompt
• Full-length books for summarization, Q&A, or fact extraction
• Extended agentic conversation histories without truncation or RAG overhead

Critically, V4-Pro's CSA + HCA architecture achieves 1M context at 27% of V3.2's inference FLOPs at that context length. The context expansion does not scale compute costs linearly.

Developers who want to run V4-Flash on their own hardware can follow the complete DeepSeek V4 Flash local setup guide for quantized model options and hardware requirements.

Which DeepSeek Model Should You Use?

The model selection guide for the broader V4 ecosystem — including comparisons against Qwen 3, Kimi, and GPT-5 — is covered in the full DeepSeek V4 specs and alternatives guide.

Open Source and License

Both V4-Pro and V4-Flash are released under the MIT license with open weights available on Hugging Face. Organizations can self-host, fine-tune, and redistribute the models without licensing fees or mandatory API dependencies.

The open-weight release alongside a Huawei chip integration announcement signals DeepSeek's intent to build a hardware-agnostic deployment story beyond NVIDIA's ecosystem — a significant consideration for teams operating in regulatory environments with chip export restrictions.

DeepSeek V3 vs V4: Key Differences at a Glance

Conclusion

DeepSeek V4 delivers on the efficiency promises that V3 established. V4-Pro's 1.6T MoE architecture with CSA+HCA hybrid attention achieves a 1M token context at 27% of V3.2's inference FLOPs — a structural improvement, not a brute-force scaling. V4-Flash undercuts V3.2 on price while extending the context ceiling from 128K to 1M tokens.

For developers evaluating whether to migrate from V3.2: V4-Flash offers an immediate cost reduction with a context window upgrade, making the migration case straightforward for most production workloads. V4-Pro is the right choice when benchmark-maximizing reasoning and coding quality is the priority.

Need engineers who can integrate DeepSeek V4, fine-tune open-source LLMs, or build production AI inference pipelines? Codersera connects you with vetted AI developers who have hands-on experience with frontier model deployment and open-weight LLM infrastructure. Hire in days, not months.