The Archive

Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.6 Luna (max), and just one point behind GLM-5.2 (max) and DeepSeek V4 Pro 0813 (max) - that GLM is 753B and that DeepSeek is 1.6B parameters, and Luna is size unknown but presumably a whole lot bigger than 27B. Qwen 3.8 27B is a truly astonishing model . Via Hacker News Tags: ai , generative-ai , llms , qwen , ai-in-china , artificial-analysis

DeepSeek V4 Pro 0813 (on OpenRouter) The latest DeepSeek Pro model is now available, via API only. I had to link to OpenRouter because DeepSeek don't have any obvious announcement page for their new model. I haven't been able to confirm if they plan to release the open weights, but given the weights are available for both April's deepseek-ai/DeepSeek-V4-Pro and July's deepseek-ai/DeepSeek-V4-Flash-0731 it seems likely. Interestingly I got very different looking pelicans for the three different reasoning levels of low, medium, and high. I've not noticed this kind of difference from any other m...

Laguna S 2.1, a 118B MoE model from Poolside AI, achieves Deepseek v4 Pro performance at lower cost than v4 Flash.

DeepSeek V4 Pro pricing undercuts US models by 11–35x, challenging premium API pricing models of OpenAI and Anthropic.

User reports optimized inference performance for DeepSeek V4 Pro using ktransformers on AMD EPYC + RTX PRO hardware with tuned NUMA settings.

User successfully quantized and ran DeepSeek V4 Pro locally on AMD EPYC + RTX PRO hardware using modified llama.cpp with Q4_K_M compression.

DeepSeek V4 Pro matches GPT-5.2 on FoodTruck Bench agentic task; first Chinese frontier model, 17× cheaper, within 10 weeks of GPT-5.2 release.

Deepseek V4 Pro offers 100M tokens for $2.65, dramatically undercutting API pricing across the industry.

Community discussion comparing Kimi K2.6 and DeepSeek V4 Pro for real-world use cases, noting K2.6 strength in coding.

GPT-5.5 ranks 2nd on Extended NYT Connections Benchmark; Kimi K2.6 leads open-weights; DeepSeek V4 Pro and Qwen 3.6 Max show significant gains.

Reddit discussion seeking user feedback on DeepSeek v4 Pro coding performance versus Kimi K2.6 and GLM 5.1.

DeepSeek V4 Pro exhibits degraded token efficiency vs. V3.2 despite 2.5x scale increase, suggesting intelligence density declined.

DeepSeek releases V4 Pro (1.6T-A49B) and Flash (284B-A13B) models optimized for Huawei Ascend chips, no longer leading benchmarks.

DeepSeek V4 Pro costs 15x more to run than V3.2 on Artificial Analysis benchmarks, exceeding Gemini 3.1 Pro pricing.

DeepSeek V4 Pro shows weaker-than-expected performance on LMSYS Arena user preference voting, a crowdsourced benchmark distinct from capability measurement.