DeepSeek: DeepSeek V4 Pro

DeepSeek V4 Pro is DeepSeek's flagship model, released April 24, 2026, and offered as an open-weight alternative to closed frontier models like GPT-5.x, Gemini 3.x, and Claude Opus 4.x. It launched alongside a smaller sibling, V4 Flash, as DeepSeek's first two-tier V4 lineup.

#Architecture and Specs

• Type: Mixture-of-Experts (MoE), 1.6 trillion total parameters with ~49 billion active per token
• Context window: 1,048,576 tokens (~1M), with a max output around 384,000 tokens
• Modality: Text-only at launch (no native image/video input — for multimodal work you'd need a different model)
• Attention: Uses a hybrid attention system (built on the same underlying architecture as V4 Flash) tuned for efficient long-context processing
• Modes: Supports both "Thinking" (reasoning) and "Non-Thinking" modes, with reasoning-effort settings up to "xhigh"
• API compatibility: Works with both OpenAI- and Anthropic-style API formats
• License: Open, MIT-licensed weights

#Benchmark Performance

Reports vary somewhat by source, but generally show V4 Pro performing strongly for its price tier without topping the absolute frontier:

• Artificial Analysis Intelligence Index: scores in the 44–67.5 range depending on configuration (reasoning/max-effort variants score higher), ranked around #7–18 among tracked models
• SWE-bench Verified: 80.6% in one independent review
• MMLU-Pro: 73.5; IFBench: 76.5; GPQA Diamond: 56.5; Long-context recall: 66.3
• One source ranks it #35–46 out of ~99–110 tracked models overall, noting its strongest category is knowledge/reasoning-adjacent tasks and its weakest is raw coding relative to top-tier peers
• Throughput is moderate (60–72 tokens/second), which is slower than some competitors

#Strengths and Trade-offs

Reviewers frame V4 Pro as excellent value-per-token rather than an outright frontier leader:

• Where it shines: full-codebase analysis, multi-step automation, large-scale information synthesis, and any workload where volume drives your bill — near-frontier reasoning at a fraction of Western flagship pricing
• Where it falls short: long-running autonomous agent chains. One review found it more prone to losing track of earlier constraints or failing to re-check its own prior steps during extended multi-tool-call sessions, contrasting this with Claude Opus 4.7's more aggressive training emphasis on self-consistency and error recovery — a gap the reviewer called "real and currently decisive" for production coding agents chaining 50+ tool calls.
• It's still labeled a "preview" in production by some users despite wide deployment, so pinning to a specific version and keeping a fallback model is commonly recommended.

#Model Family Context

V4 Pro sits within a broader V4 family that includes V4 Pro (Max), V4 Pro (High), V4 Flash (Max), V4 Flash (High), and base variants — with V4 Pro (Max) aimed at the hardest reasoning tasks using longer chain-of-thought budgets, useful in cases where standard V4 Pro hits its reported ~24% timeout rate on the most demanding problems.

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) — both supporting a context length of one million tokens.

DeepSeek-V4 series incorporate several key upgrades in architecture and optimization:

1. Hybrid Attention Architecture: We design a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dramatically improve long-context efficiency. In the 1M-token context setting, DeepSeek-V4-Pro requires only 27% of single-token inference FLOPs and 10% of KV cache compared with DeepSeek-V3.2.
2. Manifold-Constrained Hyper-Connections (mHC): We incorporate mHC to strengthen conventional residual connections, enhancing stability of signal propagation across layers while preserving model expressivity.
3. Muon Optimizer: We employ the Muon optimizer for faster convergence and greater training stability.

We pre-train both models on more than 32T diverse and high-quality tokens, followed by a comprehensive post-training pipeline. The post-training features a two-stage paradigm: independent cultivation of domain-specific experts (through SFT and RL with GRPO), followed by unified model consolidation via on-policy distillation, integrating distinct proficiencies across diverse domains into a single model.

DeepSeek-V4-Pro-Max, the maximum reasoning effort mode of DeepSeek-V4-Pro, significantly advances the knowledge capabilities of open-source models, firmly establishing itself as the best open-source model available today. It achieves top-tier performance in coding benchmarks and significantly bridges the gap with leading closed-source models on reasoning and agentic tasks. Meanwhile, DeepSeek-V4-Flash-Max achieves comparable reasoning performance to the Pro version when given a larger thinking budget, though its smaller parameter scale naturally places it slightly behind on pure knowledge tasks and the most complex agentic workflows.

#Contact

If you have any questions, please raise an issue or contact us at [email protected].