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Thinking Machines Lab has released Inkling 2 Small , the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index , it scores 31. Its predecessor, Inkling Small , scores 13 on the same index. The model is live on Tinker for fine-tuning and inference. TL;DR Size: 594B total, 17B active MoE; 1M-token context. Runs on: Tinker API only for now. Self-hosting hardware is not disclosed. Weights support MXFP8 and NVFP4. Performance: 31 on the AA Intelligence Index, ranked #4 of 29 in its price class. Best: 86.3% on SWE-Bench Pro v2 (Hard), second only to Kimi K3 in the lab’s table. Worst: 13.1% on Terminal-Bench 4.0, versus 32.8% for GLM-5.3-Flash. What is Inkling 2 Small? Inkling 2 Small is a multimodal MoE model from Thinking Machines Lab fo...

Google has released Gemini Omni 1.2 Flash , the newest version of its video generation and editing model. Gemini Omni 1.2 Flash generates and edits clips up to 15 seconds natively. It also cuts the price by 30% compared with Omni 1.1 Flash. The model is generally available on Google Cloud and the Gemini API . For developers, the takeaways are longer clips, fewer re-renders and a lower bill per second. TL;DR Runs on: Google-hosted only (Gemini API, Google Cloud). No open weights, no local deployment. Performance: Google reports better reference adherence and less drift. No 1.2 benchmark scores are published. Best: Native 15-second clips at $0.07 per second of 720p video, about $1.05 per 15-second clip. Worst: Google says fast sports action, rapid script changes and complex physics still need work. What is Gemini Omni 1.2 Flash? Gemini Omni 1.2 Flash is a multimodal video model from Google DeepMind. It takes text, images, short video clips and audio as...

Baidu has released ERNIE 5.5 Flash Base , the raw pre-training checkpoint of its new sparse Mixture-of-Experts language model. ERNIE 5.5 Flash Base is a 280B-parameter text model from Baidu that activates 14B parameters per token. The weights stop at the end of pre-training, with no mid- or post-training applied. This as a clean starting point for teams that want to compare fine-tuning, instruction tuning or RL methods from one checkpoint. TL;DR Size: 280B total, 14B active per token; 128K training context, 1M for fine-tuning and inference. Runs on: Multi-GPU servers. Baidu’s reference configs use 4-way parallelism in SGLang and vLLM. GPU model and VRAM not disclosed. FP8 and BF16 weights. Performance: Best score on 24 of 30 base-model benchmarks in Baidu’s unified pipeline (self-reported). Best: MMLU-Pro 78.4, ahead of DeepSeek-V4-Flash-Base at 69.2. Worst: HELMET-Rerank 56.2, behind DeepSeek-V4-Flash-Base at 60.0. What is ERNIE 5.5 Flas...

OpenAI just shipped Images 2.6 across ChatGPT, Codex and the API. For developers, the core release is GPT-Image-2.6, shipped as two API models: Flare and Sunburst. OpenAI team states that Images 2.6 generates images 50% faster than before. The update also adds GIF output, SVG export, markup-guided edits and a new complexity parameter. TL;DR Size: Closed weights, token-billed image API. Runs on: OpenAI-hosted only (ChatGPT, Codex, API). Performance: 50% faster image generation than the previous release, per OpenAI. Best: Flare can return an image in as little as 6 seconds at low quality. Worst: No benchmark scores and no per-image cost table published for 2.6. image source: openai What is GPT-Image-2.6? GPT-Image-2.6 is OpenAI’s image generation and editing model family for the API. It accepts text and image inputs and returns images. GPT-Image-2.6 Flare is built for fast iteration and interactive workflows. GPT-Image-2.6 Sunburst is b...