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AIGoogle released EmbeddingGemma 2, a 740M-parameter multimodal embedding model under Apache 2.0 for private, on-device search and retrieval. It maps text, code, images, video, and audio into one shared space and reports a 9.92-point gain over EmbeddingGemma 1 on MTEB Code. The post lists about 191MB active RAM for quantized text-only weights and about 567MB for the full multimodal model on a Pixel 11 Pro.

AIArena (@arena) shared a link to its full Agent Arena overall leaderboard at arena.ai. The post provides no scores, rankings, or other details beyond directing readers to the leaderboard page.

AIShanghai AI Lab has open-sourced Intern-Decision, a family of 0.8B, 2B and 4B parameter models that return structured decisions with probabilities instead of free text. The developers self-report that the 4B model averages 90.02% accuracy across seven test suites, ahead of a commercial reference model at 88.74%, with about 44 milliseconds of local latency on a single RTX 4090. Weights are on Hugging Face, and MetaX says the models run on its hardware from launch.

AIOpenAI announced that GPT-6 is being rolled out to all ChatGPT users, alongside a new Intelligent UI. The excerpt provides no further details on capabilities, availability timing, or how the interface works.

AIAnthropic has released Claude Haiku 5.5, priced at one tenth of the previous Haiku 4.5 and one twentieth of Sonnet 5.5. The source excerpt is cut off before listing the full comparison, so the comparison with OpenAI's pricing is not confirmed here.

AIAnthropic released Claude Haiku 5.5, which the article says outperforms DeepSeek V4.1 Flash and GLM-5.3-Flash on official benchmarks and matches GPT-6 Luna on price. On OSWorld 2.1, its Low effort tier scores 42.0% at $0.07 per task, versus 15.7% at $1.45 for Haiku 4.5 at Max. Migrating from Haiku 4.5 requires changes to thinking configuration, sampling parameters, assistant prefill, and the computer-use tool version.

AIAnthropic released Claude Haiku 5.5, a small model priced at about a quarter of Haiku 4.5's running cost. Anthropic also halved Sonnet 5.5 cache read prices from 20 cents to 10 cents per million tokens.

AIXiaomi released the MiMo-V2.5-TTS Series, three speech synthesis models for stock voices, voice design, and voice cloning. The models accept natural-language style instructions and inline audio tags, and the source says the three models are free of charge for a limited time on the Xiaomi MiMo API platform. Xiaomi also open-sourced integration Skills for agent applications on GitHub.

Why it matters: The release shows how a TTS family adds style instructions, inline audio tags, and voice design or cloning to speech synthesis, which matters for agent and creative workflows.

AIXiaomi MiMo has released MiMo-V2.5-ASR, an open-source speech recognition model that the company says achieves state-of-the-art results across multiple benchmarks. The model supports bilingual Chinese–English recognition, Chinese dialects such as Wu, Cantonese, Hokkien, and Sichuanese, code-switching, and lyrics transcription. It is also designed to handle noisy environments and multi-speaker conversations.

AIXiaomi introduces the MiMo-V2.6 series of models, as stated on its official MiMo page. The source excerpt gives only the tagline, describing frontier intelligence across all modalities, so specific capabilities, benchmarks, and availability cannot be confirmed from it.

AIMiniMax says its open-source H3 model is almost on par with closed-source state-of-the-art video world models on physics. The claim is supported by a quoted benchmark, World Models' Last Exam in Physics, where eight leading models scored at most 57.76/100 across 40 physics tasks, and free-fall videos averaged only 26.61/100 on composite scores.

AIArtificial Analysis shares the second part of an AA-Video-T2V v2.0 prompt describing a four-shot documentary-style handheld video of a glass cabin in falling snow. The shots follow a caretaker sweeping snow from the deck, empty snow-covered windows, an empty interior, and the same caretaker stamping snow off his boots at the door, with hard cuts between shots.

AIArtificial Analysis reports that Grok Imagine Video 1.5 Lite comes closest to the frontier in Architecture & Real Estate, Consumer, and Productivity & Knowledge Work use cases. It sits furthest from the frontier in Live-Action Film and Frontier use cases. Against Grok Imagine Video 1.5, Lite matches it in Social Media & Creator Content and trails it on the other nine use cases.

AIArtificial Analysis shares an AA-Video-T2V v2.0 text-to-video prompt, a 10-second product-page scene of a presenter revealing a volcano-shaped mist diffuser. The prompt specifies a shift from warm daylight to dim evening lamplight, fixed close-up camera angles, and consistent presenter and product appearance across shots.

AIAmong 12 models on AA-Video-T2V-Silent v2.0, Grok Imagine Video 1.5 Lite is the only one that is both fastest and highest quality, with no model beating it on both measures. It generates a 10-second 1080p clip in a median of 60.5 seconds. Kling 3.0 1080p (Pro) scores slightly higher but takes 94 seconds for a 5-second clip, while Vidu Q3 Turbo is 9 seconds faster on a 5-second 720p clip yet scores well below it.

AIGrok Imagine Video 1.5 Lite ranks #17 on AA-Video-T2V v2.0, two places above Google's Veo 3.1. At 1080p with audio, it costs $0.14 per second versus $0.40 per second for Veo 3.1. Compared with Grok Imagine Video 1.5, Lite is 44% cheaper at 1080p but ranks six places lower.

AISpaceXAI's Grok Imagine Video 1.5 Lite ranks #17 on both AA-Video-T2V v2.0 leaderboards, ahead of Google's Veo 3.1 at about a third of its price. It is the fastest model at its quality level in Artificial Analysis benchmarks, with a median of 60.5 seconds for a 10-second 1080p clip, and it costs $0.14 per second at 1080p, 56% of Grok Imagine Video 1.5's $0.25 per second.

AIEpoch AI reports that OpenAI halved GPT-6.1 Sol's cached-input price compared with GPT-6 Sol. Its measurements also show the model handles long prompts faster. The post raises, but does not confirm, a new architecture for GPT-6.1 Sol.

AIAnthropic has cut Sonnet 5.5 cache read pricing in half to $0.10 per million tokens, with input at $2 and output at $10 per million tokens. The company says this makes Sonnet 5.5 roughly 20% cheaper on most agentic work. The change applies to API usage only and does not alter Claude Code usage limits.

AIAnthropic's Claude Haiku 5.5 reached 1587 points on the Arena WebDev leaderboard, a 257-point gain over Claude Haiku 4.5's 1330. The post says it also improved significantly across all categories.

AIGPT-6 Sol (Daybreak Blue, max) has been added to the Artificial Analysis Cyber Index as a trusted-access model and ranks #1 on the Index. Compared with the publicly available GPT-6 Sol, it shows its largest gains on CyberGym-E2E, the benchmark where the most safety refusals are observed.

AIArtificial Analysis added trusted-access models to its Cyber Index, and GPT-6 Sol (Daybreak Blue, max) now ranks first. The model is available only through OpenAI's Daybreak program and records no safety blocks across the Index. Its overall score is 32 points higher than the publicly available GPT-6 Sol (max), at a cost of $1.77 per task versus $11.67 for Grok 4.7 (xhigh).

AIThe Arena Alignment Index, built from over 90K real-world agent sessions across 27 models, ranks OpenAI's GPT-6.1-Sol first with a score of 87.9, ahead of Claude-Opus-5.5 at 83.2 and Grok-4.7 at 82.7. GPT-6.1-Sol also posted the lowest observed rates across the index's three signals: 0.89% Unauthorized Action, 1.98% False Attribution, and 2.34% Deceptive Completion. The index's authors report that newer models consistently outperform their predecessors across all four labs, suggesting broad progress in agent safety.

AIOpenAI's API pricing for GPT-6.1 Sol in Ultrafast mode is $12 per million input tokens and $60 per million output tokens. The mode is built for time-sensitive work such as debugging outages, agents navigating apps, and live experiences where speed matters.

AILightOn has released LightOnOCR-3, a family of OCR models in 0.8B and 4B versions that it says lead benchmarks including OlmOCR-Bench and ParseBench, with the 0.8B model positioned as the sub-1B option. The models recognize text, handwriting, images, charts and document structure in one pass, process documents up to twice as fast as LightOnOCR-2, and are released under the Apache 2.0 license.

AIOdyssey has launched Odyssey-3, its most powerful foundation world model, with a public research preview. Odyssey-3 Pro scored 66.1 on Physics-IQ Verified video-to-video with best-of-8 sampling, the highest reported result. The model generates environments from prompts and predicts changes in real time as users move through scenes.

AIOdyssey released Odyssey-3 Pro, which sets a new top score on Physics-IQ Verified, a benchmark where models continue videos of real physics experiments. In robotics, a robot arm with tens of hours of demonstrations recovered from a missed grasp, a behavior absent from those demos.

AIGeneralist has announced GEN-1.5, its latest foundation model for the physical world. The post provides only a link to the company's blog for further details, so no specifications, benchmarks, or availability information can be confirmed from this source.

AIGeneralist's GEN-1.5 is shown repeatedly packing bottles into boxes, one after another. The post links to a blog post with more details about the model.

AIOdyssey 3 Pro, a world model, is now live as a research preview and ranks first on the Physics-IQ Verified video-to-video benchmark. The post says it can learn from visual observations and map that knowledge to physical controls for robots, cars, video games, and drones. Odyssey-3, the model launched alongside it, is described as free to try.

AIOdyssey has released Odyssey-3, which it describes as a major leap toward world models that power physical AI, generate training environments, and enable new human experiences. The post invites readers to try Odyssey-3 at the company's website but gives no specific benchmarks, parameter counts, or pricing.

AIOdyssey says its Odyssey-3 model's learned world knowledge can be adapted by physical AI developers to control robots, power humanoids, drive cars, and fly drones. The post describes this as a capability for autonomous machines generally, without providing benchmarks, specifications, or availability details.

AIOdyssey announced Odyssey-3, a foundation world model it says enables applications in physical AI, human experiences, and training intelligences. The company highlights agents learning from experience inside Odyssey-3 while working toward objectives.

AIOdyssey-3 Pro achieved a score of 66.1 on Physics-IQ Verified's video-to-video benchmark, the highest reported score so far. Physics-IQ evaluates physical behavior across fluid dynamics, optics, solid mechanics, magnetism, and thermodynamics.

AIOdyssey has launched Odyssey-3, which it describes as its most powerful foundation world model yet. The company says the model sets a new state of the art on Physics-IQ and can power robots, train AIs, and generate interactive experiences, and it is available to try for free today.

AIOdyssey-3 is a new world simulator that generates interactive environments from a prompt in real time. It learns representations of physics, dynamics, and cause-and-effect from a broad dataset of visual observations.

AIPerplexity's Decider V1.1 ranked first on DecisionBench while also having the lowest cost, according to a post highlighting the result. The benchmark results cited include 949 shared text cases, 93.9% accuracy, a 534 ms median latency, and $0.016 per 1k decisions.

AIJetBrains has released Mellum2.1, a 12B mixture-of-experts thinking model with 2.5B active parameters, under Apache 2.0 on Hugging Face. Post-training reinforcement learning in real software repositories raised SWE-bench Verified from 2.0 to 47.0, according to JetBrains' self-reported results. Qwen3.5-9B still leads on SWE-bench Pro, GPQA Diamond and AIME, and GGUF builds start at 7.0 GB for local use.

AICarbon-A is an open model that predicts gene locations directly from DNA, and it has been used to annotate genomes from over 22,000 species. The release includes a database of 566 million gene candidates, about 16 times the gene annotations in the RefSeq dataset. Wet-lab RNA experiments supported 239 candidates missing from RefSeq across cats, Syrian hamsters, chickens, and Arabidopsis.

Why it matters: The source ties an open gene-annotation model to specific wet-lab checks and gene counts, helping readers judge how far its predictions extend beyond well-studied genomes.

AIThe team released Carbon-A, an open model that finds genes directly in DNA, along with a database of 566.34 million candidate genes across 22,617 species. The model reads genomes without needing a close relative, and wet-lab validation in cats, chickens, and arabidopsis is cited, with 239 genes found missing from reference annotations of common species. The authors say the model marks gene locations but does not design DNA or predict gene function.