IBM releases Granite 4.1-8B with 131K context window and enhanced tool-calling capabilities
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.
Granite 4.1 8B — Quick Specs
IBM releases Granite 4.1-8B with 131K context window and enhanced tool-calling capabilities
IBM has released Granite 4.1-8B, an 8-billion parameter instruction-following model with a 131,072-token context window. The model was released on April 29, 2025, under an Apache 2.0 license.
Performance benchmarks
According to IBM, Granite 4.1-8B achieves the following scores:
- Code tasks: 85.37% on HumanEval pass@1, 87.30% on MBPP pass@1, 79.88% on HumanEval+ pass@1
- General tasks: 73.84% on MMLU 5-shot, 80.51% on BBH 3-shot with chain-of-thought
- Math tasks: 92.49% on GSM8K 8-shot, 80.10% on Minerva Math 0-shot with CoT
- Tool-calling: 68.27% on BFCL v3
- Alignment: 87.06% on IFEval average
The model is part of a three-model family including 3B and 30B parameter versions.
Technical specifications
Granite 4.1-8B uses a decoder-only dense transformer architecture with:
- 4,096 embedding size
- 40 layers
- 32 attention heads with 8 key-value heads
- Grouped Query Attention (GQA)
- RoPE positional embeddings
- SwiGLU activation in MLP layers
- 12,800 MLP hidden size
Training and capabilities
IBM trained the model on a combination of open source instruction datasets with permissive licenses and internally generated synthetic data. The post-training pipeline included supervised fine-tuning and reinforcement learning alignment.
The model supports 12 languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. According to IBM, it can be fine-tuned for additional languages.
Key capabilities include:
- Text summarization and classification
- Question-answering and RAG
- Code generation and completion
- Function calling with OpenAI-compatible tool definitions
- Fill-in-the-middle code completions
- Multilingual dialog
The model achieves 64.84% on MMMLU 5-shot across 11 languages and 58.89% on INCLUDE 5-shot across 14 languages.
Safety benchmarks
IBM reports safety scores of 95.80% on SALAD-Bench and 81.19% on AttaQ for the 8B model.
Availability
The model is available on Hugging Face under the Apache 2.0 license. Pricing for API access has not been disclosed. IBM provides code examples for both basic text generation and tool-calling use cases using the Transformers library.
What this means
Granite 4.1-8B represents IBM's push into the competitive 8B parameter model space with strong code performance and a notably large 131K context window. The Apache 2.0 license and multilingual support position it as an alternative to models like Llama 3.1 8B and Mistral 7B for enterprises requiring permissive licensing. The tool-calling improvements and comprehensive benchmark suite suggest IBM is targeting production AI assistant deployments, though actual inference costs and API availability remain unclear.
Related Articles
IBM's Granite PatchTST-FM-r2 Tops Permissively Licensed Time Series Forecasting Models on GIFT-Eval
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.
Tencent Open-Sources AuK, a 1.5B-Parameter Speech Generation and Editing Model
Tencent has open-sourced AuK, a 1.5B-parameter foundation model for speech generation and editing that handles TTS, content editing, and audio enhancement through natural-language instructions. The release includes a distilled AuK-Flash variant for 4-step fast inference, both under MIT license.
AllSpark's Iris-mini and Iris-pro Top Open-Weight Search Agent Benchmarks
Chinese lab AllSpark has released Iris-mini and Iris-pro, two open-weight search agents built on Qwen3 models that claim the top spot among open-weight systems in their size classes on four research benchmarks. The release includes model weights, an agent harness, and evaluation code, with training pipelines to follow.
Google Releases TimesFM-3, a 330M-Parameter Model That Forecasts Sales Using Weather and Discount Data
Google Research has released TimesFM-3, a 330-million-parameter time series forecasting model that predicts outcomes like sales by combining related variables, historical data, and known future events such as discounts or weather. The model claims top rankings on three benchmarks against Amazon's Chronos-2 and the Toto-2.0 family.
Comments
Loading...