Ai Benchmarks

DeepSeek releases open-weight V4.1-Flash-1017, with search APIs at $0.75 per 1,000 calls: Deepseek released V4.1-Flash-1017, an open-weight model it says outperforms its predecessor on benchmarks, runs faster and returns shorter outputs. It also opened its search engine via Search and Fetch APIs at $0.75 per 1,000 calls; users in Claude Code and Codex get free access for now.

AI Models • The Decoder

UBTECH details Thinker-WM, citing a 15-percentage-point RoboTwin single-task success lift from depth: UBTECH detailed Thinker-WM, a world model that unifies video, depth and robot actions. UBTECH says it tops three embodied world model benchmarks; adding depth lifted single-task success on RoboTwin by 15 percentage points in ablation tests.

Robotics • Pandaily

Google's Gemini 4 Argon scores 97.5% on ARC-AGI-3 but 49.8% under the standard harness: Google DeepMind's Gemini 4 Argon scored 97.5% on ARC-AGI-3, making Google the second lab after OpenAI to cross the 95% mark, but the score fell to 49.8% in the standard harness. It also trails rivals in math and software engineering.

AI Benchmarks • The Decoder

Meta's Muse Spark Open is an 816B-parameter open-weight multimodal model: Meta’s Muse Spark Open is a new open-weight model with 816 billion parameters that processes text, images, video, and audio with up to one million tokens of context. It performs roughly on par with the most capable open-weight Chinese AI models on benchmarks and is optimized to run locally on Apple's M5 Ultra.

Meta • The Decoder

Study finds LLM reasoning uses 3.7× more inference energy on average: A study measured inference energy for 86 open-weight LLM checkpoints across seven benchmarks and six hardware platforms. Most configurations used a median 2.6–4.8× more energy than another configuration scoring at least as well, with gaps up to 27×; reasoning raised energy about 3.7× on average.

AI Research • arXiv