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AIAnthropic has introduced Claude Haiku 5.5, which it calls the cheapest, fastest, and most capable small model it has released. On average, running it costs around 75% less than Claude Haiku 4.5.

AIAnthropic has released Claude Haiku 5.5, available now across all platforms including Amazon Web Services, Google Cloud, and Microsoft Azure. The post links to Anthropic's announcement page for further details.

AIAnthropic says Claude Haiku 5.5 is especially good value for tasks under 100,000 tokens, which made up around 90% of requests to its previous Haiku model. The post points to cost-effectiveness for the typical workload rather than providing pricing or benchmark figures.

AIAnthropic says Claude Haiku 5.5 shows major improvements across almost all of its alignment evaluations compared with Claude Haiku 4.5. The post reports far fewer instances of misaligned behavior in the new model.

AIAnthropic's Haiku 5.5 is built for high-volume, cost-sensitive work such as summaries and classification. It can serve as a subagent alongside Claude Opus 5.5 and Sonnet 5.5 on coding tasks. It is also fast enough for live customer support and browser use.

AIAnthropic says Claude Haiku 5.5 is a significant step up over Haiku 4.5 across coding, computer use, and knowledge work. The post gives no benchmark scores, pricing, or availability details.

AIAnthropic introduces Claude Haiku 5.5, which it describes as the cheapest, fastest, and most capable small model it has released. On average, running it costs around 75% less than Claude Haiku 4.5.

AIAndrew Curran says Haiku 5.5 is live for him right now. The post gives no further details on availability, features, or pricing.

AILiquid AI and NVIDIA Robotics announced a collaboration around Liquid AI's models on NVIDIA hardware. The main post is brief and gives no further technical detail or figures.

AILiquid AI has released its d1 decision models, with d1-3B recommended for high-quality text and vision tasks and d1-omni-600M offered as an experimental option for smaller footprints. The weights are available for download, fine-tuning, and local deployment.

AILiquid AI's Open d1 models run across NVIDIA DGX, RTX, and Jetson hardware, with day-one llama.cpp support for deployment anywhere. Measured one request at a time, the d1-3B model's single-question latency is 8 ms on an NVIDIA RTX 4090, 16 ms on Jetson AGX Thor, 26 ms on Jetson AGX Orin 64 GB, and 50 ms on Jetson Orin Nano.

AILiquid AI has released d1-omni-600M, an experimental 600M-parameter model that handles text plus image or audio input. It combines LFM2.5-Encoder-350M with vision and audio encoders and leads the company's text benchmark comparison on toxicity detection and paraphrase identification. The post suggests uses such as voice-command routing, on-device moderation, and intent classification.

AILiquid AI's d1-3B ranks first among models under 10B parameters on the Decision Index v0.2.1, a benchmark for structured decision-making. Built from LFM2.5-VL-3B, it makes decisions from text and images in a single pass. It is suited to reranking, agent guardrails, and visual inspection.

AILiquid AI released Open d1, two open-weight multimodal models in its d1 decision model family. The d1-3B model supports text and vision, while d1-omni-600M supports text plus image or text plus audio. The source says the models are meant for real-time decision making across data centers, RTX workstations, and Jetson edge devices.

AILiquid AI released two open-weight decision models, d1-3B and d1-omni-600M (experimental), built on its Liquid Foundation Models and available on Hugging Face. d1-3B scores 48.57 on the Decision Index 0.2.1, the highest among decision models under 10B parameters, and answers a question in 16 ms on an NVIDIA Jetson AGX Thor and under 50 ms on a Jetson Orin Nano. The models support text and images (d1-3B) or text with image or audio (d1-omni-600M).

AIPerplexity is open-sourcing pplx-embed-v2-late, multi-vector embedding models for text and images in one shared space, in 9B and 0.6B sizes. The 9B model can index multimodal data, the 0.6B model can run queries on device, and PDF pages can be searched without OCR. The author reports 92.4% on MADQA and 64% on BrowseComp+, with weights available on Hugging Face.

AIPerplexity has published both pplx-embed-v2 models on Hugging Face, with the collection available at the linked URL. The post provides no further details on model sizes, benchmarks, or capabilities.

AIPerplexity reports its system reached 92.4% accuracy on MADQA, a benchmark of 500 questions over 800 PDFs. The post describes this as the top result among retrievers on that benchmark.

AIPerplexity has released pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages within a shared embedding space for cross-model querying. The company says both models achieve frontier performance and are publicly available on Hugging Face.

AIAi2 is making new Stage 1 checkpoints available, with the byte-level components already trained and the original model's weights left unchanged. Researchers can use them to test new architectures and train the full system without repeating that initial stage.

AISmallest AI's Pulse ranks first on the Diarization + ASR track of Voice Arena's Diarization Bench with a 24.4% DER, where lower is better. The next system in that track scores 40.7% DER, and Pulse misses 4.4% of reference speech, the lowest in the track.

AIMistral has released Mistral Large 4, nicknamed Le Chonk, a 1 trillion-parameter model it says can be used and customized by anyone. It is in preview, with a final version due by the end of the month, and is optimized for coding and cyberdefense as well as manufacturing, finance, and electrical engineering tasks. Mistral claims it is the most capable open-weight model developed outside China and says it was trained from scratch rather than through distillation.

AIMistral Large 4 is positioned to run general-purpose agents that gather information and produce finished deliverables across complex workflows. The post provides no further details on benchmarks, parameters, pricing, or availability.

AIThe Technology Innovation Institute introduces Falcon-ASR, a 1.6 billion parameter speech recognition model for Arabic with a focus on the Emirati dialect. On six Arabic test sets it reports an average word error rate of 20.92%, versus 23.17% for the best published leaderboard result it compared against. The model also transcribes English, French, Spanish and Portuguese with the same weights, and a demo Space is available while API access and native apps are planned.

AIvLLM Semantic Router and KR Labs released Vela 2.0, which makes span-level decisions covering safety checks, domain classification, PII spans, and unsupported claims in a single call. It comes in four sizes from 0.3B to 9B parameters under the Apache-2.0 license.

AIAnthropic released Claude Haiku 5.5, which it calls its cheapest, fastest, and most capable small model. It costs around 75% less to run than Haiku 4.5 and is aimed at high-volume, cost-sensitive tasks such as summaries and classification. The release also cuts Sonnet 5.5 cache read prices by 50%, and the model is available on AWS, Google Cloud, and Microsoft Azure.

AIAnthropic released Claude Haiku 5.5, which scores 43 on the Artificial Analysis Intelligence Index, up 26 points from the last Haiku release. Pricing is $0.10/$0.50 per 1M input/output tokens up to 100k tokens, rising to $0.50/$2.50 above that, but at max effort it uses about 162k output tokens per Intelligence Index task, roughly 3x GPT-6 Luna.

Why it matters: The benchmark shows Haiku 5.5 scores well but uses far more output tokens than GPT-6 Luna, so cost per task matters beyond list price.

AIGoogle's Nano Banana 2.1 is an upgraded image model that outperforms prior versions in visual design, mask-based editing, subject consistency, and natural-looking imagery. Josh Woodward calls mask-based editing his favorite feature from the launch and says more is coming soon.

AIGoogle DeepMind released EmbeddingGemma 2, an open 740M-parameter embedding model that maps text, code, images, video, and audio into one 768-dimensional space. Text-only use needs a 270M-parameter footprint, about 191MB active RAM when quantized on a Pixel 11 Pro, while loading all modalities takes about 567MB. The reported MTEB Code NDCG@10 score is 78.68, about 14% above the first generation, and MTEB Multilingual v2 is 61.36, roughly flat.

AILiquid AI released two open-weight d1 decision models, d1-3B and d1-omni-600M, on Hugging Face. d1-3B scores 48.57 on the Decision Index v0.2.1 public split and answers a single question in 8 ms on an NVIDIA GeForce RTX 4090 and 50 ms on a Jetson Orin Nano. d1-omni-600M is an experimental checkpoint that handles text with images or audio and scores 15.95 on the same index.

Why it matters: The release pairs open-weight decision models with measured latency across Apple, NVIDIA, and Jetson hardware, showing how edge deployment changes what is practical.

AIAnthropic launched Claude Haiku 5.5, which it describes as the cheapest, fastest, and most capable small model it has released, aimed at high-volume, cost-sensitive tasks. Max and Team plans now include monthly API credits for running their own apps and agents on the Claude Platform, rolling out over a few days. Users claim the credits by linking a Claude Console organization in Settings > Billing for Max or Organization settings > Billing for Team.

Why it matters: The notes name a new small model and a credit change for Max and Team plans, with the claim path, which matters for teams budgeting API use.

AIGoogle DeepMind has released EmbeddingGemma 2, an open 740 million parameter model that maps text, images, audio, and video into one embedding space. It is built on the Gemma 4 architecture under an Apache 2.0 license and supports an 8K token context window. The company reports a code benchmark gain from 68.76 to 78.68 on MTEB Code and says the model can run on-device with about 567MB of active RAM for the full multimodal version on a Google Pixel 11 Pro.

Why it matters: The release shows how a 740M-parameter embedding model can cover text, code, images, audio, and video on local hardware, with memory and storage figures to compare against other on-device options.

AIA 9B model trained with SFT and prompt repetition at about $25 of compute reaches roughly 79% of Jev's Decision Index score. It ranks second among all models at 10B parameters or below and 14th of 70 overall.

AIGoogle releases EmbeddingGemma 2, its first native multimodal embedding model, built on Gemma 4 under Apache 2.0. It embeds over 100 languages, code, images, audio, and video into one vector, with an 8,192-token context and four sizes from 270M to 740M parameters. Matryoshka output dimensions of 768, 512, 256, or 128 are supported, and the model is available in Sentence Transformers and LiteRT-LM, with a reported 14% gain on MTEB Code.

Why it matters: The release extends an embedding model to text, code, images, audio, and video in one vector, a useful option for retrieval systems that mix media types.

AIMistral Large 4 solved 18 of 19 challenges in a CTF speedrun, with tool calls and solve times drawn from actual runs. The post frames the model as efficient at reasoning over diverse complex challenges compared with other models.

AIMistral AI says its Mistral Large 4 is one of the world's strongest AI models for cybersecurity. The post gives no benchmark scores, comparisons, or availability details.

AIPerplexity says its open-weights multimodal decision model pplx-decider-v1.1-27b ranks first on the new Hugging Face Decision Index 0.3 benchmark. The updated model is available to all, and it costs $0.02 per million input tokens, half the price of v1.

AIOn ARC-AGI-2, Grok 4.7's best score is 61.4% at $2.01 per task, down from Grok 4.6's 67.1% at $0.76 per task. The post notes cost per task rose 166% despite identical input and output token pricing.

AISpaceXAI's Grok 4.7 scored 90.2% on ARC-AGI-1 at $0.64 per task, higher than Grok 4.6, according to ARC Prize. It reached 61.4% on ARC-AGI-2 at $2.01 per task and 1.8% on ARC-AGI-3 under the standard harness ($2.7k), or 10.0% with the provider adapter harness ($4.8k), both lower than Grok 4.6 on those two benchmarks.

AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.