#DeepSeek

AIInferact and the vLLM community reported a 1.9× low-concurrency speedup and about 5.3× throughput under a 150 TPS constraint for DeepSeek-V4.1-Flash over three weeks. Gains came from SWA bounded replay with CUDA graphs, which cut TTFT by about 30%, and from integrated DeepSeek kernels such as MegaAttention, Mega-mHC, Mega-Gate, and DeepSelect. The post measures these results on the SemiAnalysis AgentX benchmark.

Why it matters: The post breaks down how SWA bounded replay and fused kernels cut prefill and decode costs, a reusable engineering pattern for long-context agentic serving.

AIChinese AI companies earn about 10% of the combined AI-related revenue of OpenAI and Anthropic, according to Epoch AI as of September 2026. Their main income streams are consumer apps, API access, enterprise and government deployments, licensing fees, and AI-complemented businesses such as cloud and advertising. Releasing model weights lets third-party hosts compete on price, which weakens API margins for model-focused firms like Z.ai and DeepSeek.

Why it matters: The piece maps how Chinese AI firms earn revenue and why open-weight releases weaken API pricing, giving context for comparing them with US frontier labs.

AIEpoch AI estimates that compute built from projected 2025 to 2027 high-bandwidth memory shipments could support tens to hundreds of millions of frontier AI agents, or billions of cheaper ones. Running nonstop, the top-tier agents would match the working hours of 140 million to 700 million full-time employees, and the central DeepSeek V4 Pro estimate of about 1.9 billion agents would match 8 billion workers.

Why it matters: The estimate converts memory shipments into agent capacity and revenue ranges, showing how hardware supply could translate into labor and sales if demand keeps up.

AIEpoch AI estimates that high-bandwidth memory shipped in 2025–27 could eventually support about 30–170 million concurrent frontier-model agents once fully deployed and allocated. Using DeepSeek V4 Pro serving benchmarks, the estimate rises to about 1.9 billion concurrent agents. The authors compare the implied API-equivalent spending of $2.6–5.3 trillion per year with projected developer revenue of roughly $1 trillion by end-2027, suggesting demand may lag supply.

Why it matters: The analysis converts HBM shipment data into concurrent agent capacity and compares it with projected API revenue, showing where compute buildout may outpace demand.

AIThe vLLM team trained a DSpark speculative decoding draft model for Kimi K3, a 2.8T-parameter model, using the Speculators library on GB300 NVL72 hardware. They added a MooncakeHiddenStatesConnector to stream hidden states from disaggregated vLLM inference nodes to training nodes across multiple machines. The released speculator raises single-stream interactivity from about 110 to about 435 tokens per second per user on math reasoning, with up to about 3.5x higher output throughput under concurrent load.

Why it matters: The post shows how hidden-state extraction and Mooncake transfers let a 2.8T-parameter model's speculator be trained across multiple nodes, a reusable pattern for similar setups.

AIFireworks AI reports that DeepSeek-V4.1-Flash scores 74.34% pass@1 on DeepSWE at $0.430 per task, close to GPT-6-Astra's 74.12% at $6.524. On Terminal-Bench 2.1 it scores 86.5% against Astra's 87.5% at about 12x lower cost per task, while on HLE it trails Astra alone at 34.52% versus 50.40%. The post also reports that a combined oracle router reaches 54.80% on HLE, and that serverless and dedicated API access is available with US-hosted endpoints coming soon.

AIDeepSeek released open weights for V4.1-Flash, which Baseten now offers through its Model APIs. The model has 552B total parameters, 8B active for prefill and 16B for decode, a 1M token context window, and text plus image input. Its Causal Encoder-Decoder design runs only the encoder during prefill and reuses a projected KV cache, and the source reports the global KV cache at a quarter of V4-Flash's memory.

Why it matters: The post explains how the CED architecture splits prefill and decode compute and cuts KV cache memory, which matters for coding agent costs.

AIDeepSeek officially released DeepSeek-V4.1-Flash, the smallest model in its new architecture family, with native multimodal visual understanding. The API now serves it under the model name deepseek-flash, while V4 Flash and V4 Flash Vision Exp were retired and routed to V4.1 Flash. API prices were reduced with the release, and V4 Pro remains available after September 14, 2026.

Why it matters: The release lists benchmark results alongside API model-name changes and retirements, so developers can check both capability claims and migration steps.

AIDeepSeek released DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model with 552B backbone parameters and support for contexts up to one million tokens. The technical report says its global KV cache footprint is 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash, and reports 8B activated parameters per token during prefill and 16B during decode.

Why it matters: The report shows KV cache per token falling to about one quarter of DeepSeek-V4-Flash, a concrete tradeoff between long-context serving cost and benchmark results.

AIDeepSeek introduces DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model in the DeepSeek-V4 family, built on V4-Flash with visual modules. It reports substantial gains over DeepSeek-V4-Flash-0731 on multimodal agent benchmarks, such as ApexBench at 36.5 versus 26.2, while keeping text agent performance comparable. The repository provides tokenizer files, prompt encoding, vLLM and SGLang serving instructions, and is licensed under MIT.

Why it matters: The source compares the model with its text-only predecessor and Opus-4.8 on agent benchmarks, showing where vision gains occur and where text performance holds.

AIDeepSeek V4 Pro 0813 scored 95.2% on SWE-Bench Verified, ahead of Kimi K3 at 92.6% and Fable 5 at 85.4%, in Fireworks AI's eval runs. It costs $0.309 per solved task on SWE-bench versus $0.808 for Fable 5, and it is available through Fireworks serverless and dedicated endpoints, with SFT, DPO, and RFT training support. Its 1M-token context window and native tool calling target long-horizon agentic workloads, though its Java accuracy on Aider Polyglot (48.9%) trails Fable 5 (74.5%).

AIDeepSeek V4 Pro 0813 recorded zero refusals across 840 adversarial security tasks in CyberGym testing, solving them at about half the cost per success of the top-scoring model tested, Kimi K3. In the 697-task common cohort, V4 Pro reached a 53.7% reward rate at $2.50 per solved task, versus 47.6% and $9.64 for GPT-5.5 and 5.9% and $33.28 for Claude Opus 4.8.

AIDeepSeek has made DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal vision understanding model, available on its API platform via model='deepseek-v4-flash-vision-exp'. The source says its pure-text capabilities are on par with DeepSeek-V4-Flash, while it shows a significant leap on agent benchmarks requiring visual understanding, which it says brings multimodal agent capabilities close to Opus-4.8.

Why it matters: The source gives benchmark scores and a model identifier, so readers can compare the experimental vision model against the text-only DeepSeek-V4-Flash on agent tasks.

AIDeepSeek has made DeepSeek-V4-Pro generally available on its app, web, and API, with the API model name set to deepseek-v4-pro. The release reports agent benchmark results, including 87.9 on Terminal Bench 2.1 and 74.1 on Toolathlon-Verified. It also adds native OpenAI Responses API support, low/high/max thinking effort levels, and off-peak API prices set at half of peak prices starting 16:00 UTC on August 16, 2026.

Why it matters: The update pairs new agent benchmark results with API format and pricing changes, so developers can judge both capability and cost impact before migrating.

AIDeepSeek has released DeepSeek-V4-Pro-0813 as the official version superseding the V4-Pro preview, built on the preview structure with a DSpark speculative decoding module. The model scores higher than the preview on the listed benchmarks, including Terminal Bench 2.1 at 87.9 and DeepSWE at 62.7, and the weights are under the MIT License.

Why it matters: The release reports agent benchmark gains over the preview and lists vLLM and SGLang setup, useful for judging deployment cost and fit.

AIDeepSeek has released DeepSeek-V4-Flash-0731 as the official version superseding the preview, with substantially enhanced agentic capabilities. The source reports it outperforms DeepSeek-V4-Pro (Preview) on listed benchmarks, including Terminal Bench 2.1 at 82.7 versus 72.1, despite a far smaller activated parameter count. The model ships under the MIT License with DSpark speculative decoding supported in vLLM and SGLang.

Why it matters: The release shows benchmark gains over the preview and a concrete vLLM and SGLang serving path, useful for teams weighing a self-hosted agentic coding model.

AIDeepSeek has released the DeepSeek-V4-Flash API in public beta, and developers can use the latest version by setting the model name to deepseek-v4-flash. The source reports agent benchmark results far above V4-Pro-Preview, including 82.7 on Terminal Bench 2.1 and 70.3 on Toolathlon verified. V4-Flash natively supports the Responses API format and is adapted for Codex, while V4-Pro and the APP/WEB models are unchanged.

Why it matters: The release lists agent benchmark results against V4-Pro-Preview and notes Responses API support for Codex, which helps developers gauge the upgrade's practical effect on their workflows.

AIPromptArmor reports that Skills in Microsoft Copilot Cowork can call DeepSeek even when an organization has not opted into the DeepSeek Preview. The calls use the agent's own access path, so users need no API key, and a Skill built this way received a 100/100 score from Microsoft's Skill Scanner. After Microsoft removed the DeepSeek Preview setting on June 25, the report says admins had no remaining setting to block DeepSeek through the Cowork code environment, leaving disabling Cowork entirely as the only option.

AIDeepSeek has posted the eagle3_gemma4_12b_ttt7 model on Hugging Face, listed at 2B parameters in BF16 tensor format with 621 downloads last month. The model card is empty and no Inference Provider currently deploys it, while it appears in the DeepSpec collection of 12 items.

AIDeepSeek published a model named eagle3_qwen3_14b_ttt7 on Hugging Face, listed at 2B parameters in BF16 with the Safetensors format. The model has no model card, is not deployed by any Inference Provider, and recorded 400 downloads last month.

AIDeepSeek has published eagle3_qwen3_8b_ttt7 on Hugging Face, a model listed at 2B parameters in BF16 safetensors format. The model card is missing, it has 577 downloads last month, and no Inference Provider currently deploys it.

AIDeepSeek has published eagle3_qwen3_4b_ttt7 on Hugging Face, a 0.9B-parameter model in BF16 tensor format. The model has no model card and is not deployed by any Inference Provider, and it is listed in the DeepSpec Collection.

AIOpenRouter introduced Fusion, a tool that sends a prompt to a panel of models and has a judge model fuse their results into one answer. On 100 DRACO deep research tasks, a Fable 5 and GPT-5.5 panel scored 69.0%, above Fable 5 alone at 65.3%, and a budget panel of Gemini 3 Flash, Kimi K2.6, and DeepSeek V4 Pro reached 64.7% at about half the cost of Fable 5.

Why it matters: The source gives benchmark scores, panel compositions, and contamination controls, letting readers judge how much of the gain comes from model diversity versus self-synthesis.

AIThe DeepSeek API now supports V4-Pro and V4-Flash through both the OpenAI ChatCompletions and Anthropic interfaces. Developers keep the same base_url and set the model parameter to deepseek-v4-pro or deepseek-v4-flash. The legacy names deepseek-chat and deepseek-reasoner will be discontinued on 2026-07-24, and until then they map to the non-thinking and thinking modes of deepseek-v4-flash, respectively.

Why it matters: The source gives exact model names, an unchanged base URL, and a July 2026 discontinuation date, so developers can plan their migration from legacy names.

AIDeepSeek has upgraded both deepseek-chat and deepseek-reasoner to DeepSeek-V3.2. deepseek-chat maps to the model's non-thinking mode, while deepseek-reasoner maps to its thinking mode.

AIDeepSeek-V3.2-Speciale is available through a temporary endpoint at the same pricing as V3.2, but without tool-call support. The endpoint remains available until Dec 15, 2025, 15:59 (UTC).