DeepSeek Search scores 69 on the Artificial Analysis Search Index, ranking 17th among 27 search products, and is the lowest-priced paid search API on the list. It charges $0.75 per 1k searches, with a per-task cost of about $0.032, ranking 5th cheapest among 27 products. On AA-Omniscience its accuracy is 70%, ranking 10th, but on BrowseComp, which requires multi-hop reasoning, it is only 63%, ranking 25th; each search returns about 7k tokens, raising model cost to the 2nd highest on the list.
Bloomberg reports that DeepSeek has completed a funding round of about $15 billion, one of the largest private financings in China. During negotiations, its funding target was raised at least twice, from about $7.5 billion to $12 billion; CATL and Tencent are the largest investors, with CATL reportedly contributing about $1.5 billion, twice that of the first round. Chinese media put the pre-money valuation at about $74.8 billion, unchanged even as the funding size doubled, meaning the new capital obtained a larger stake at the same price. Together with the first round of more than $7.5 billion completed in June, DeepSeek has raised a total of about $22.5 billion in roughly six months.
Bloomberg reports that DeepSeek has completed a funding round of about $15 billion, one of the largest private financings in China. During negotiations, its funding target was raised at least twice, from about $7.5 billion to $12 billion; CATL and Tencent are the largest investors, with CATL reportedly contributing about $1.5 billion, twice that of the first round. Chinese media put the pre-money valuation at about $74.8 billion, unchanged even as the funding size doubled, meaning the new capita
Vercel has added DeepSeek v4.1 Flash (1017) to Token Pass, using DeepSeek's new weights, supporting a 1M context window with no configuration changes required. Guillermo Rauch says the model is fast, extremely low-priced, and offers zero data retention (ZDR), making it suitable for running sub-agents whose tasks are planned by larger, more expensive models.
DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it requires Nvidia GPUs with the SM100 architecture or Huawei Ascend 950 NPUs, so it is not aimed at individual users.
DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it r
The U.S. Department of Homeland Security (DHS) has ordered federal civilian agencies to immediately stop procuring DeepSeek products and services and to remove them from government information technology systems; the order applies to civilian executive branch agencies, excludes national security systems, and does not restrict public use of DeepSeek. DHS says the DeepSeek model has multiple threat vectors, with reasons including servers located in China, account registration collecting personally identifiable information, multiple critical vulnerabilities found in the iOS app, and prompt interactions potentially allowing sensitive data to leak out. China's Ministry of Commerce responded that it consistently opposes over-broadening the concept of national security and politicizing science, technology and trade issues, and stated it will take all necessary measures to safeguard the legitimate rights and interests of Chinese enterprises.
The U.S. Department of Homeland Security (DHS) has ordered federal civilian agencies to immediately stop procuring DeepSeek products and services and to remove them from government information technology systems; the order applies to civilian executive branch agencies, excludes national security systems, and does not restrict public use of DeepSeek. DHS says the DeepSeek model has multiple threat vectors, with reasons including servers located in China, account registration collecting personally
DeepSeek has open-sourced its internal inference framework DeepInfer, which it says powers the high-throughput, low-latency inference of DeepSeek-V4.1-Flash. The framework supports both Nvidia and Huawei Ascend hardware, and offers capabilities such as distributed inference, speculative decoding, SWA Bounded Replay, and structured output. DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with the official API.
Citing DeepSeek production data disclosed by Latent.Space, swyx says a single cluster processes 155T tokens per day with a 96% input cache hit rate, yielding $38 in revenue per GPU-hour at off-peak prices. He believes this raises the lifetime value of every GPU sold by about 30%, corresponding to roughly $600 billion in market value potential, and jokes that the more you buy, the more you earn.
DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.
DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also
DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Ascend 950. DeepSeek says components such as KV cache management and load balancing are not fully open-sourced because they are coupled with internal infrastructure, while the vLLM team reports that under matched GB200 configurations, decoding throughput differs from DeepInfer's published results by less than 6%.
DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Asce
Yuchen Jin, citing data disclosed by DeepSeek, said that a single cluster currently processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour (off-peak pricing). He noted that DeepSeek has not only open-sourced its inference engine but also disclosed revenue figures, though it did not specify which GPU models were used; DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. Based on an estimated B300 rental price of $7 per GPU-hour, the gross margin is approximately 82%.
Yuchen Jin, citing data disclosed by DeepSeek, said that a single cluster currently processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour (off-peak pricing). He noted that DeepSeek has not only open-sourced its inference engine but also disclosed revenue figures, though it did not specify which GPU models were used; DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. Based on an estimated B300 rental price of $7 per GPU-hour, the gross margin is approximately 82
Arena announced that DeepSeek-V4.1-Flash-20261017 (Max) has entered the Arena, ranking first among open-source models in both the Agent Arena and Code Arena: WebDev leaderboards. Arena also mentioned that the 3D world generated by this model, as well as more detailed ranking information, can be viewed in the related post.
DeepSeek has open-sourced its internal large-scale cloud service inference framework DeepInfer and the production-grade token codec component deepseek-recipe. The former supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and can run on NVIDIA and Huawei Ascend platforms. DeepSeek says the high-throughput, low-latency inference service of DeepSeek V4.1 Flash is based on this framework, with a single cluster serving 155 trillion Tokens/day in throughput, at 38 US dollars per card per hour calculated at off-peak prices. The processing logic of deepseek-recipe is fully aligned with the official DeepSeek API. The team also explained that the KVCache management and load balancing components are deeply coupled with internal infrastructure, so the relevant code has not yet been fully open-sourced.
DeepSeek has open-sourced its internal large-scale cloud service inference framework DeepInfer and the production-grade token codec component deepseek-recipe. The former supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and can run on NVIDIA and Huawei Ascend platforms. DeepSeek says the high-throughput, low-latency inference service of DeepSeek V4.1 Flash is based on this framework, with a single cluster serving 155 trillion Tokens/day in throughpu
DeepSeek has open-sourced DeepInfer, its in-house inference framework used to serve its models in production. Released under the Apache 2.0 license, it lets developers run DeepSeek-V4.1-Flash on their own infrastructure, with support for subsequent models planned. The framework includes optimized compute kernels and communication libraries such as FlashMLA, DeepGEMM, and DeepEP, supports speculative decoding, asynchronous scheduling, and structured output, and also open-sources the deepseek-recipe library (available in Rust and Python versions) for handling request and response transformations. On the hardware side, it supports Nvidia SM100 (Blackwell) architecture and the Huawei Ascend 950 NPU series from day one; older architectures such as Hopper are not listed. Batch-invariant inference and deterministic sampling are available only on Nvidia hardware, and the published performance data is also based on Nvidia GPUs. The current version supports offline inference but lacks complete production serving components such as KV cache management, cross-machine inference stage separation, and replica load balancing.
DeepSeek has open-sourced DeepInfer, its in-house inference framework used to serve its models in production. Released under the Apache 2.0 license, it lets developers run DeepSeek-V4.1-Flash on their own infrastructure, with support for subsequent models planned. The framework includes optimized compute kernels and communication libraries such as FlashMLA, DeepGEMM, and DeepEP, supports speculative decoding, asynchronous scheduling, and structured output, and also open-sources the deepseek-reci