IBM

IBM has released Granite Time Series PatchTST-FM-r2, a 385M-parameter forecasting model that ranks #2 overall among replicable zero-shot models on the GIFT-Eval benchmark and first among those with permissive, commercial-friendly licensing. The model uses conformer-based architecture and supports probabilistic forecasts across up to 8,192-step contexts.

IBM has released Granite 4.2 8B, a dense reasoning model built for math, code generation, and agentic workflows. The model supports 131K context, 12 languages, and three switchable reasoning modes, priced at $0.10 per 1M input tokens and $0.15 per 1M output tokens.

IBM has released Granite 4.2, a family of open-weight language models in 3B, 8B, and 30B parameter sizes, trained on roughly 15 trillion tokens with context windows up to 512,000 tokens. The 8B and 30B variants underwent additional 'agentic RL' training for tool use, code execution, and web search.

IBM has published a technical breakdown of Granite 4.2, its first dense, decoder-only reasoning model family, released in 3B, 8B, and 30B sizes. The models are pre-trained on roughly 15 trillion tokens, extended to a 512K-token context window, and post-trained with a multi-stage RL pipeline that includes agentic tool-use training for the 8B and 30B variants.

IBM has released two new 470M-parameter Granite Speech models that transcribe audio at over 12,600x real-time speed on an NVIDIA H200 GPU. The encoder-only models achieve 4.85-5.00% word error rate while running over 20x faster than prior Granite Speech versions.

IBM Research has launched the Open Agent Leaderboard, the first open benchmark that evaluates complete AI agent systems rather than just underlying models. The leaderboard reveals that agents using identical models can achieve significantly different success rates and costs depending on system architecture, with failed runs costing 20-54% more than successful ones.

IBM released two new multilingual embedding models under Apache 2.0: a 97M-parameter compact model scoring 60.3 on MTEB Multilingual Retrieval (highest in its size class) and a 311M full-size model scoring 65.2. Both support 200+ languages with enhanced retrieval for 52 languages, handle 32K-token context (64x increase over predecessors), and include code retrieval across 9 programming languages.

IBM released Granite Embedding 311M Multilingual R2, a 311-million parameter dense embedding model with 32,768-token context length and support for 200+ languages. The model scores 64.0 on Multilingual MTEB Retrieval (18 tasks), an 11.8-point improvement over its predecessor, and ships with ONNX and OpenVINO models for production deployment.

IBM has released Granite Speech 4.1 2B, a 2-billion-parameter speech-language model trained on 174,000 hours of audio for automatic speech recognition and translation across English, French, German, Spanish, Portuguese, and Japanese. The model introduces a dual-head CTC encoder and includes variants for speaker attribution and a novel non-autoregressive architecture for higher throughput.

IBM has released the Granite 4.1 family of language models under Apache 2.0 license. The models come in 3B, 8B, and 30B parameter sizes. Unsloth has released 21 GGUF quantized variants of the 3B model ranging from 1.2GB to 6.34GB.

IBM released Granite 4.1 30B, a 30-billion parameter instruction-following model with a 131,072 token context window. The model scores 80.16 on MMLU 5-shot and 88.41 on HumanEval pass@1, with enhanced tool-calling capabilities following OpenAI's function definition schema.

IBM has released Granite 4.1 8B, an 8-billion-parameter decoder-only language model with a 131,072-token context window. The model supports 12 languages and costs $0.05 per million input tokens and $0.10 per million output tokens, available under the Apache 2.0 license.

IBM has released Granite 4.1-8B, an 8-billion parameter long-context model with a 131,072-token context window. The model achieves 85.37% on HumanEval and 73.84% on MMLU 5-shot, with enhanced tool-calling capabilities reaching 68.27% on BFCL v3. Released under Apache 2.0 license, it supports 12 languages.

IBM released Granite 4.1, a family of dense decoder-only LLMs (3B, 8B, 30B parameters) trained on approximately 15 trillion tokens using a five-phase pre-training pipeline. The 8B instruct model matches or surpasses the previous Granite 4.0-H-Small (32B-A9B MoE) despite using fewer parameters and a simpler dense architecture. All models support up to 512K context windows and are released under Apache 2.0 license.

IBM has launched Bob, its AI coding assistant, following internal testing with 80,000 employees. The company claims teams saw average productivity gains of 45% across complex workflows. Pricing ranges from $20 to $200 per month using a "Bobcoin" credit system.

IBM announced Granite 4.0 3B Vision, a 3 billion parameter vision-language model designed for enterprise document processing. The model achieves 86.4% on Chart2Summary and 92.1% TEDS score on cropped table extraction, shipped as a LoRA adapter on Granite 4.0 Micro to enable modular text-only fallbacks.