Which AI model actually fits the job? You won't know until you experiment. The Alibaba Cloud Token Plan makes it easier to try, test and compare, with: · One credit pool across Qwen, Wan, HappyHorse, DeepSeek and GLM · Text, audio, image and video capabilities · Clearer visibility into your AI usage And it’s easy to start, with your first month priced at just $4. Explore the Token Plans: #AlibabaCloud# #TokenPlan# #Qwen# #Wan# #HappyHorse# #DeepSeek# #GLM# #MultiModelAI# #GenerativeAI# #AIWorkflow#

Why manage separate AI tools for the script, the image, and the video? The Alibaba Cloud Token Plan gives you one shared credit pool across supported models and tools, with visibility into usage and access to newer models like Qwen3.8-Max-Preview, HappyHorse1.1, DeepSeek V4, and GLM-5.2. One plan for every modality, to build more, spend less. Get started from just $4 in your first month. Explore the Token Plans: #AlibabaCloud# #TokenPlan# #Qwen# #Wan# #HappyHorse# #GenerativeAI# #AIContent# #MultimodalAI#

Video AI has been reasoning over blurred and occluded footage while trusting every frame equally. This work tackles that blind spot. Title: Confidence-Aware Tool Orchestration for Robust Video Understanding URL: ❓ What's the problem? 💡 Video-LLMs implicitly assume every frame is equally reliable (the authors' "Blind Trust Problem"). When footage degrades from motion blur, glare, or occlusion, they fail to notice and lose 15-30 points on real-world benchmarks, while their self-reported confidence barely changes, a silent failure. ❓ How does Robust-TO solve it? 💡 It bakes per-frame trustworthiness into every reasoning stage. First, quality profiling scores blur, brightness, and occlusion to keep only reliable frames; then it decomposes the query into sub-queries routed to tools robust to the dominant corruption, and every tool returns a (result, confidence) pair. ❓ How is confidence used? 💡 Evidence is grouped into high/medium/low tiers. High drives the conclusion, medium is kept only when consistent, low is a fallback only, and any residual uncertainty is stated in the answer. It is trained with GRPO using a confidence-cost reward. ❓ How well does it work? 💡 56.4% average on clean video (+10.6pt over Gemini-2.5-Pro) and 54.3% under corruption (+5.8pt over the strongest open-source Video-R1). It also trims frames from 32 to 20.7, cutting inference time by over 35% while gaining +1.6pt accuracy. #VideoUnderstanding# #MultimodalAI#

🔥 Big News｜Kimi Pre-IPO Allocation Now Live — Start from Just 100U 📈 Why Kimi? Kimi K3, just released on July 16, features 2.8 trillion parameters, native multimodality, and a 1M token context window — placing it among the world’s top-tier AI models. Limited Pre-IPO allocation available — first come, first served. 👉 Check details: 👉Subscribe here:

Moonshot’s Kimi K2.6 is the new leading open weights model. Kimi K2.6 lands at #4# on the Artificial Analysis Intelligence Index (54) behind only Anthropic, Google, and OpenAI (all 57) Key takeaways: ➤ Increase in performance on agentic tasks: @Kimi_Moonshot's Kimi K2.6 achieves an Elo of 1520 on our GDPval-AA evaluation, which is a marked improvement over Kimi K2.5’s Elo of 1309. GDPval-AA is our leading metric for general agentic performance, measuring the performance on knowledge work tasks such as preparing presentations and analysis. Models are given code execution and web browsing tools in an agentic loop via our open source reference agentic harness called Stirrup. This continues Kimi K2.6’s strength in tool use, maintaining a 96% score on τ²-Bench Telecom, placing it among other frontier models in this category. ➤ Low hallucination rate: Kimi K2.5 scores 6 on the AA-Omniscience Index, our knowledge evaluation measuring both accuracy and hallucination rate. This score is primarily driven by a comparatively low hallucination rate of 39% (reduced from Kimi K2.5’s 65%), indicating a greater capability to abstain rather than fabricate knowledge when the model is uncertain. Kimi K2.6’s low hallucination rate places it similarly to other models such as Claude Opus 4.7 (36%) and MiniMax-M2.7 (34%) ➤ High token usage: Kimi K2.6 demonstrates high token usage, but is in line with other frontier models in the same intelligence tier. To run the full Artificial Analysis Intelligence Index, Kimi K2.6 used ~160M reasoning tokens. This is slightly lower than Claude Sonnet 4.6 (~190M reasoning tokens) but much higher than GPT 5.4 (~110M reasoning tokens). ➤ Open weights: Kimi K2.6 is a Mixture-of-Experts (MoE) model with 1T total parameters and 32B active, same as the previous two generations of models Kimi K2 Thinking and Kimi K2.5. Kimi K2.6 again pushes the open weights frontier in intelligence. ➤ Third Party Access: Kimi K2.6 is accessible through Moonshot’s First Party API as well as third party API providers Novita, Baseten, Fireworks, and Parasail ➤ Multimodality: Kimi K2.6 supports Image and Video input and text output natively. The model’s max context length remains 256k. Further analysis in the threads below.