DeepSeek released two open-source models, V4-Pro and V4-Flash, which are already running in production environments worldwide (including internally at Lightning AI). An initial assessment from a Lightning AI optimization engineer: "Coding is my primary use case—it's good."
DeepSeek-V4-Pro: 1.6T total parameters and 49B active parameters, designed for deep reasoning and agentic coding
DeepSeek-V4-Flash: 284B total parameters and 13B active parameters, designed for speed and high-throughput pipelines
Both support a 1M-token context window as standard
| Dimension | V3.2 | V4 |
|---|---|---|
| Context window | 128K | 1M (standard) |
| KV cache | baseline | 10% of V3.2 (a hybrid compressed sparse attention architecture combining CSA + HCA) |
| Inference FLOPs | baseline | 27% of V3.2 (at 1M tokens) |
| Reasoning between tool calls | stateless, restarting each time | persistent, retaining the full chain of thought; a 20-step pipeline does not lose context |
| SWE-bench Verified | ~69% | Pro 80.6% / Flash 79.0% |
| DeepSeek V4-Pro | DeepSeek V4-Flash | DeepSeek V3.2 | Claude Opus 4.6 | |
|---|---|---|---|---|
| Context | 1M | 1M | 128K | 1M |
| Input price/1M | $1.74 (cache miss) / $0.145 (hit) | $0.14 (miss) / $0.028 (hit) | $0.28 / $0.028 | $5.00 / $0.50 |
| Output price/1M | $3.48 | $0.28 | $0.42 | $25.00 |
| Open source | ✅ MIT | ✅ MIT | ✅ MIT | ❌ |
| Speed (approx.) | ~33 tokens/s | 60+ tokens/s | ~35 tokens/s | Moderate |
| SWE-bench Verified | 80.6% | 79.0% | ~69% | 80.8% |
| LiveCodeBench | 93.5% | 91.6% | — | 88.8% |
| Codeforces | 3206 | 3052 | — | Not reported |
| Terminal Bench 2.0 | 67.9% | 56.9% | — | 65.4% |
| GPQA Diamond | 90.1% | 88.1% | — | 91.3% |
V4-Pro scores 80.6% on SWE-bench Verified, just 0.2 points behind Claude Opus 4.6, while its output price is approximately one-seventh of the latter's ($3.48 vs $25)
V4's hybrid compressed attention (CSA/HCA) makes the economics of a 1M-token context practical
Persistent reasoning across tool calls is V4's key upgrade for Agent scenarios
Pricing and benchmark scores come from the DeepSeek V4 technical report (Max reasoning mode) and Anthropic's pricing page
DeepSeek V4 Flash
