DeepSeek V4 Flash (lightweight tier) · model id: deepseek-v4-flash
Scale: 284B total parameters · 13B active parameters
Context: 1M tokens · maximum output 384K
Pricing: input (cache miss) $0.14 / 1M tokens; input (cache hit) $0.0028 / 1M tokens; output $0.28 / 1M tokens
DeepSeek V4 Pro (flagship tier) · model id: deepseek-v4-pro
Scale: 1.6T total parameters · 49B active parameters
Context: 1M tokens · maximum output 384K
Pricing: input (cache miss) $0.435 / 1M tokens; input (cache hit) $0.003625 / 1M tokens; output $0.87 / 1M tokens
Availability: open-source weights are available through Hugging Face and Ollama Cloud, or via the official DeepSeek chat service for free. The API is compatible with OpenAI and Anthropic formats (https://api.deepseek.com · /anthropic), and supports JSON mode, tool calling, FIM completion, and chat-prefix completion.
⚠️ The old model IDs deepseek-chat and deepseek-reasoner will be deprecated on 2026-07-24 (mapping to V4 Flash's non-thinking and thinking modes, respectively).
DeepSeek compares V4 with Gemini 3.1 Pro and GPT-5.4; it makes no claims about Claude Opus 4.8 (Opus 4.8 was released after V4, and online claims that "V4 beats Opus 4.8" are all third-party comparisons)
Real-world experience: the model is partly "benchmark maxed"—strong on standard tests, but not consistent enough in actual use
Community rankings: on Code Arena, deepseek-v4-pro ranks #35 and its thinking variant ranks #31, behind domestic models such as GLM 5.1 and Kimi K2.6 as well as leading Western models
Calling V4 "mid" on Twitter (especially in the Chinese AI community) triggered a strong backlash. Objectively speaking, the V4 family currently ranks below GLM 5.1, Kimi K2.6, and leading Western models (including the later-released Opus 4.8) on public coding leaderboards; where V4 truly wins is price per token, by a huge margin.
Pros: 1M ultra-long context · fully open source under the MIT license · extremely low pricing · the Flash model punches above its weight · strong performance on 360°/3D rotation tasks Cons: feels "benchmark maxed" · rough real-world execution · trails GLM 5.1 and Qwen 3.6 Plus · frontend output looks dated · the Pro model fails in complex agentic workflows
Its price, context, and efficiency make DeepSeek V4 an excellent foundation for future development, but the preview release needs serious polishing. Cheaper does not mean better—just cheaper.
DeepSeek V4 Flash
