IBM

IBM Research held the model constant (open-weight GLM-5.3-flash) and varied only the agent harness on 90 AppWorld tasks. CUGA, IBM's own open-source harness, passed 81.1% of tasks, against 46.7% for Hermes and 30.0% for Deep Agents.

IBM released Granite Switch 4.1.1, a patch release that is fully backwards compatible with 4.1 and requires no migration. It updates the Granite Speech ASR model (10 languages) and the Decision adapter in granitelib-core, while the base model stays unchanged.

IBM and Kaggle are partnering on a continuously updated public leaderboard for ScarfBench, IBM's open benchmark for coding agents doing enterprise Java framework migrations. The benchmark covers migrations across Jakarta EE, Quarkus, and Spring, according to an IBM Research technical note published October 27, 2026.

IBM Research released Granite Timeseries Ensemble R1, a recipe that combines four pre-trained Granite forecasters into one median and quantile forecast with no retraining or fitted weights. On 97 GIFT-Eval configurations, the Probabilistic Ensemble scores 0.683 MASE, against 0.685 for the best single member.

IBM released granite-speech-5.0-2b, a 2B-parameter speech-language model trained on 390,000 hours of public audio. IBM claims it ranks #2 among open-weight ASR models with a 5.06% average English WER. IBM also updated its 470M Turbo CTC model to 19,870 RTFx and refreshed the weights of granite-speech-4.1-2b-plus.

IBM Research says its ALTK-Evolve method took a stock ReAct agent running GPT-4.1 to 67.5% Pass^4 on τ²-bench retail, above every retail entry in its October 12, 2026 leaderboard snapshot. The method appends learned guidelines to the system prompt and updates no model weights.

IBM has released Granite-Switch-4.1-8B and Granite-Switch-4.1-30B, models that bundle a Decision adapter, a Guardian safety adapter and a speech-to-text model behind a single endpoint. IBM says the combined model uses roughly the same GPU VRAM as the base model alone. Benchmark claims are IBM's own, and context window and pricing were not disclosed.

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 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 introduces ALTK-Evolve, an agentic memory system that matches or beats ACE's accuracy on the AppWorld benchmark while cutting inference token costs by up to 86% through selective guideline retrieval instead of full-playbook injection.

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.