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DeepSeek V4
Open LLM for coding, reasoning, and agentic workflows
DeepSeek-V4 lets developers run frontier-class coding, reasoning, and agentic AI on open weights with a 1M token context window. Two model sizes, MIT licensed, API-compatible with OpenAI and Anthropic. For developers and AI researchers.
The gap between open and closed models on hard benchmarks just got a lot smaller.
DeepSeek-V4 is a family of open-weight language models built for coding, reasoning, and agentic tasks. V4-Pro runs 1.6T total parameters with 49B active. V4-Flash runs 284B total with 13B active. Both ship with 1M token context as the default, under MIT license, with weights on HuggingFace.
Most open models have trailed closed ones on the benchmarks that matter for production use: competitive coding, multi-step reasoning, agentic task completion. V4-Pro-Max scores 93.5 on LiveCodeBench, above both Gemini-3.1-Pro and Claude Opus-4.6 on the same eval. That is not a narrow win on a curated benchmark. That is a meaningful shift in what open-source can do.
The architecture explains part of why this is possible at cost. A new hybrid attention mechanism drops single-token inference FLOPs to 27% and KV cache to 10% of what DeepSeek-V3.2 required at 1M-token context. Efficiency gains at this scale are what make open deployment economically viable.
Three reasoning modes, Non-Think, Think High, and Think Max, let you tune latency against accuracy without switching models. The API is live today and accepts OpenAI and Anthropic-format requests, so migration is a single model name change.
For developers building agent pipelines, researchers benchmarking open models, and teams that want closed-model performance without the lock-in.
About DeepSeek V4 on Product Hunt
“Open LLM for coding, reasoning, and agentic workflows”
DeepSeek V4 was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #127 on the daily leaderboard. DeepSeek-V4 lets developers run frontier-class coding, reasoning, and agentic AI on open weights with a 1M token context window. Two model sizes, MIT licensed, API-compatible with OpenAI and Anthropic. For developers and AI researchers.
On the analytics side, DeepSeek V4 competes within API, Open Source and Developer Tools — topics that collectively have 678.2k followers on Product Hunt. The dashboard above tracks how DeepSeek V4 performed against the three products that launched closest to it on the same day.
Who hunted DeepSeek V4?
DeepSeek V4 was hunted by Raghav Mehra. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of DeepSeek V4 including community comment highlights and product details, visit the product overview.
Raghav Mehra
The gap between open and closed models on hard benchmarks just got a lot smaller.
DeepSeek-V4 is a family of open-weight language models built for coding, reasoning, and agentic tasks. V4-Pro runs 1.6T total parameters with 49B active. V4-Flash runs 284B total with 13B active. Both ship with 1M token context as the default, under MIT license, with weights on HuggingFace.
Most open models have trailed closed ones on the benchmarks that matter for production use: competitive coding, multi-step reasoning, agentic task completion. V4-Pro-Max scores 93.5 on LiveCodeBench, above both Gemini-3.1-Pro and Claude Opus-4.6 on the same eval. That is not a narrow win on a curated benchmark. That is a meaningful shift in what open-source can do.
The architecture explains part of why this is possible at cost. A new hybrid attention mechanism drops single-token inference FLOPs to 27% and KV cache to 10% of what DeepSeek-V3.2 required at 1M-token context. Efficiency gains at this scale are what make open deployment economically viable.
Three reasoning modes, Non-Think, Think High, and Think Max, let you tune latency against accuracy without switching models. The API is live today and accepts OpenAI and Anthropic-format requests, so migration is a single model name change.
For developers building agent pipelines, researchers benchmarking open models, and teams that want closed-model performance without the lock-in.