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esults for themselves. All of the data is open source and the community has the opportunity to draw their own conclusions on the current state of real world inference performance. DeepSeek V4 Pro 0813 DeepSeek V4 Pro 0813 is an ultra popular frontier open weight model from China. It has 1.6 Trillion parameters with 49 Billion active parameters. As of August 21, the following graph shows the best performance per SKU for all submissions, normalized by total cost of ownership (TCO). The ISL/OSL dis

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Comparing open vs. closed models across the eras of frontier models, Is the gap narrowing? The past two months have been a breakout period for open source AI. Yes, there was the “DeepSeek moment” back in January 2025, but no one actually used R1 to do any economically valuable work. In contrast, models like GLM 5.3 and Kimi K3 are genuinely capable of many of the same coding and agentic tasks that rocketed Anthropic to $65B+ ARR. Unlike others who inflated ARR, our figures were much closer to re

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he runtime determines what that agent is allowed to do. The harness layer is a spectrum rather than a fixed category. Codex and Claude Code are opinionated harnesses, while Pi and DeepSeek Harness (DSH) expose more of the harness as a programmable substrate. Through Cordis, DSH enables core behaviors that can be composed and replaced as plugins. This programmability makes the harness a poor place for a security guarantee: a layer designed to be modified cannot reliably enforce controls against i

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e cloud agents in Cursor. I took the top 1k research papers of the last year, summarized them, and visualized them. Fun fact: all 1,000 papers cost a total of $4 to summarize with DeepSeek V4 Flash. 4:44 PM · Aug 19, 2026 · 44.7K Views 37 Replies · 90 Reposts · 1.03K Likes Niels Rogge @NielsRogge My life has become boring All I do is asking coding agents to write a PLAN.md, then /grill-me on it to improve the plan, then spin up an agent to implement it I'm just a markdown manager now😭😭 2:18 PM ·

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why changing the effort invalidates the KV cache. I think closed GPT models call reasoning effort “juice,” since you can ask most models how much juice they have. In DwarfStar for DeepSeek with max reasoning this is added to the system prompt: Don’t Think The destination of reasoning tokens is therefore a learned convention: the model is trained to keep scratch work out of the final channel. Trick it into thinking it is in that channel and it may leak tokens. We have even seen older models, when

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g the generation pause within 3–5 seconds. Verified Day-0 Model Support Miles lands new frontier models on the day their weights become public, together with SGLang. Kimi-K3 [13], DeepSeek-V4 [10], Inkling [12], Qwen3.8 [14] and NVIDIA Nemotron 3 Ultra [11] were all trainable in Miles on release day, because the inference path in SGLang and the RL recipe in Miles are brought up in parallel rather than one after the other. Day-0 coverage is not limited to one architecture family either: it spans

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ing is getting more complex and more abstracted. The current open model ecosystem is buoyed by an explosion in interest in post-training open models. These people take models like DeepSeek V4 Flash, Inkling Small, or GLM 5.X and finetune them for their specific agentic tasks (e.g. in Tinker, the most popular finetuning API today). Over the last few years, post-training largely referred to the whole process of modifying the base model to make it intelligent and usable. There is a shift happening

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are the only accounts on the Hub where the heavy band carries the volume. Effectively all of MiniMax's 2026 downloads are of models above 70B, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%. The difference becomes clearer in total downloads. Moonshot’s frontier-only portfolio recorded 37M downloads over the

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Systematic Study of QKV Variants Explore and manage experiment logs in a web interface Explore code, traces, and workspace with an interactive logbook More Articles from our Blog DeepSeek-V4: a million-token context that agents can actually use 60 April 24, 2026 agents smolagents nlp Our Transformers Code Agent beats the GAIA benchmark 🏅 101 July 1, 2024 Community It was interesting to reproduce one of the ICML accepted papers using these tools. This analysis from the hackathon was particularly

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ent levels of intelligence.” Lightning also comes at a time when open models continue to roll into the market, particularly from vendors from China, such as Moonshot AI’s Kimi K3, DeepSeek’s various models, and Alibaba’s Qwen family. Stateside, Meta Glimmer this week rolled out Muse Glimmer, the first in a promised set of open models. (Meaning open weights, but not necessarily open source code for the training algorithm for the models.) Briski noted that over the past year, Nvidia has rolled out

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rence above. Where does the accuracy come from? The by-difficulty breakdown tells two different stories: Figure 1. Post-memory Task Goal Completion by difficulty, ours vs. ACE. On DeepSeek-V3.2 (right) we win Easy, Hard, and Overall; ACE only edges Medium. On gpt-oss-120b (left) ACE leads easy and medium, but per-task selection wins the hard tasks — and the aggregate. Each system improves from its own no-memory baseline (see the by-difficulty reference tables under Method notes below). The two m

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controllable agents better suited for real AI workflows. Consider a system of models performing a computer-use task measured by the Terminal-Bench Hard benchmark (Figure 1). While DeepSeek V4 has the highest overall accuracy in this example, it isn’t the best model for every task group. For instance, Kimi K2.6 is better suited to the ML and RL task groups, while Qwen3.5 397B A17B is preferable for math and science. The remaining six task groups should use DeepSeek V4. This approach can also be a

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tion the early knowledge distillation literature of 2015 to 2-3 key insights that got us to the multi-teacher on-policy distillation (MOPD) of models like Xiaomi MiMo-V2-Flash and DeepSeek V4. The second half of this book goes into a tour of over-optimization, regularization, evaluation, and character training, which is all about the many ways post-training can go wrong and what you need to stay on top of. This is what differentiates the book most from those that are just a list of code exercise

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en is available through the Hugging Face SDKs - huggingface hub ( = 1.26.1) for Python and @huggingface/inference for JavaScript. The following examples show how to use the latest DeepSeek V4 Flash through Baseten. Use a Hugging Face token to authenticate - the request will be routed to Baseten automatically. From your favorite Agent Harness Hugging Face Inference Providers are integrated in most Agent Harnesses - including Pi, OpenCode, Hermes Agents, OpenClaw, and more. This means you can plug

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cutives cited model cost as the primary obstacle to generative AI adoption. Chinese labs have shipped some of the most aggressively priced, yet competitive open models. The latest DeepSeek-V4-Flash costs just 1/105 of Anthropic’s Fable 5 on one task, according to Artificial Analysis. Even at that ratio, DeepSeek’s CEO said in a investor meeting that the company still runs a 60% profit margin and can recoup a model’s cost within 10 months. A growing trend today is developers now route models by c

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ghts the US-China gap and growing players in the open ecosystem. To date, our primary efforts on Interconnects have been release recaps for popular models like Kimi K3 , GLM 5.2 , DeepSeek R1 , etc. and monthly round-ups of the open models that matter, Artifacts Log . We’re expanding on these, building on the tools and internal data we’ve collected for other projects like The ATOM Project (and report ). This allows us to capture our ecosystem view of open models, develop methods for understandin

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runs as a minimal CLI with tree-structured sessions that allow branching and navigation without losing context; Broad provider support via BYOK: Pi connects to Anthropic, OpenAI, DeepSeek, Gemini, Groq, xAI, OpenRouter, and Kimi through your own API key or existing subscription; Small core: Pi keeps its built-in orchestration and tool surface minimal, which can make it more straightforward to inspect and modify than larger agent frameworks; No governance layer: Pi has no plan mode, sub-agents,

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idelines: a co-design checklist that helps model developers raise inference throughput and interactivity on NVIDIA GPUs. Stay tuned for a post covering sparse attention. Figure 1. DeepSeek-R1 prefill time breakdown at 4K, 32K, and 128K context lengths, where the attention share rises from 18% to 85% Every analysis is grounded in two sources: analytical formulas from GEMM-shape arithmetic, and measured data from prefill and decode kernels with FP8 for both attention compute and the KV cache. Pref

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55X GPU accelerator and made some vaguer claims about performance at the rack level compared to Vera-Rubin racks coming later this year. Here is the inference throughput using the DeepSeek V4 Flash model: As with other GPUs and XPUs, the increase in capacity across all fronts in the MI455X device yields higher token throughput, and particularly at the higher interactivity levels where users need more tokens processed. Here is how AMD reckons the cost per token on DeepSeek V4 Flash on the two dif

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oduce avoidable train-inference mismatch Why Blackwell-Native Recipes? Previous low-precision approaches were not designed around MXFP8 or NVFP4. The existing Miles path follows a DeepSeek-V3-style block-scaled FP8 recipe: weights use 128x128 block scaling, activations use 1x128 tile scaling, and scales are computed online for each tile or block. This is a strong Hopper-era recipe, but on Blackwell its FP32 scales are still applied in software around the Tensor Core path rather than through nati

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