DeepSeek-V4-Pro-Base

Headlines · 4

1. 4d
   Ant open-sources Ling-3.1-flash-1019 and Ling-3.1-flash-base

   Ant’s Bailing team released the Ling-3.1-flash-1019 checkpoint for personal-agent workloads and simultaneously published BF16 Ling-3.1-flash-base weights under the MIT license. Both use a 560B-total, 25B-active mixture-of-experts design. The 1019 checkpoint supports a context window of up to 1 million tokens and scored 44 on Artificial Analysis Intelligence Index v4.3.2, three points above the previous version. Bailing says the base model achieved the highest score in 32 of 47 tests against DeepSeek-V4-Pro-Base and MiMo-V2.5-Pro-Base, but the supplied reports do not establish independent reproduction of that comparison.

   r/LocalLLaMA · 🔥 0
2. Oct 23
   Mirendil-Base trained by one researcher and AI agents

   Mirendil says one researcher used AI agents to pretrain the 100B–500B-parameter, code-focused Mirendil-Base from scratch in 3.5 months for less than $10 million, including agent token usage, data processing, and research experiments. The company says agents wrote more than 99% of the code, while the researcher set direction, evaluated results, and intervened at key points; training used public data without purchased datasets, distillation, or synthetic data. Mirendil’s tests reported that Mirendil-Base ranked second behind Kimi K3 on cross-entropy across seven held-out code repositories, ahead of Nemotron-550B-Base, GLM-5.3, and DeepSeek-V4-Pro-Base.

   36氪 人工智能 · 🔥 0
3. Oct 19
   Microsoft unveils 2.8T-parameter MAI-Base-2 model

   Microsoft announced on October 15 that it completed pre-training for MAI-Base-2, a mixture-of-experts foundation model with 2.8 trillion total parameters and 80 billion active parameters. It was trained on 50 trillion tokens, including text and visual data, using 16,000 Nvidia GB200 GPUs for 38 days. Microsoft says the model outperformed the open-weight models included in its internal evaluations and estimates its training approach was 4.2 to 12.3 times more compute-efficient than DeepSeek-V4-Pro-Base when matching performance on selected tests. The company has not published equivalent comparisons against every leading proprietary model, and its efficiency figures estimate the compute required for comparable performance rather than showing universal superiority.

   TechRepublic · 🔥 0
4. Oct 15
   Microsoft completes MAI-Base-2 pretraining, claims it beats tested open-weight models

   Microsoft said on October 15 that it had completed pretraining of MAI-Base-2, a new base model it says outperforms all open-weight models tested. The mixture-of-experts model has 2.8 trillion total parameters and 80 billion active parameters, and was trained for 38 days on 6,000 Nvidia GB200 GPUs with 21% average floating-point utilization and 89% effective throughput, according to the company. Microsoft claims 4x to 12x the pretraining compute efficiency of DeepSeek-V4-Pro-Base and says it has begun preparing MAI-Base-3; Microsoft AI chief Mustafa Suleyman also told employees the model beats the one powering GPT-5.6 Sol using about a quarter of OpenAI's compute, though Microsoft ran those comparisons privately and OpenAI did not comment.

   IT之家 AI · 🔥 0

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