Search “Reasoning”

OpenAI Releases GPT-6.1 Terra, Focused on Visual Reasoning and Computer Operation

OpenAI has released GPT-6.1 Terra, which the company says combines detailed visual reasoning with precise computer operation and offers clearer, more natural writing, a marked improvement over GPT-5.6 Terra. The model is priced at $1 per million input tokens and $5 per million output tokens, roughly half the price of GPT-5.6 Terra and GPT-6.1 Sol, and about 10 times that of GPT-6 Luna. Simon Willison notes that Terra's position in OpenAI's model lineup is similar to Anthropic's Sonnet—neither the cheapest nor the most expensive—making it harder to judge when choosing.

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Elorian Releases Its First Model Glimpse, Using a Visual Draft Space for Image Reasoning

Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description. The company says the model was trained on several hundred GPUs, a scale notably smaller than the frontier systems it benchmarks against, and it initially targets enterprises with large amounts of video, such as robotics, research laboratories, and media and creative firms.

StepFun Step 5 Preview demonstrates strong inference performance on NVIDIA GB300

StepFun states that Step 5 Preview demonstrates strong inference performance on NVIDIA GB300 and looks forward to developers building on it. NVIDIA AI, in a quoted tweet, calls this StepFun's largest open-weight release, with a 600B MoE model capable of handling agent tasks up to 24 hours long, reaching nearly 8K tokens/sec/GPU on NVIDIA GB300 with NVFP4, and providing an NVFP4 checkpoint as well as deployment and post-training paths for SGLang, vLLM, NeMo Automodel, and NVIDIA NIM.

Google DeepMind launches general physical reasoning model Gemini 4 Argon

Google DeepMind officially launched the general physical reasoning model Gemini 4 Argon and released a demo video of its collaboration with the Apptronik Apollo humanoid robot. The robot only watched a video demonstrating beanbag tossing, practiced repeatedly, and ultimately made 7 consecutive successful throws. The model possesses visual semantic reasoning, physical understanding, and code generation capabilities, and can be used for robot control, simulation environment construction, physical data analysis, and motion optimization. In pure reasoning mode, it scored 87% on the ERQA benchmark. The demo also covers generating MuJoCo simulation environments from robot-perspective videos, human-perspective videos, and hand-drawn sketches, as well as using simulation data to raise the real robot's grasping success rate from 56% to 75%.

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