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View details ↗DISCOVER & COMPARE
Explore AI models 75
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View details ↗K2-Horizon-7B is the medium dense member of the K2-Horizon family: a 7B-core decoder-only model with a 512K context window. - **Strong dense baseline.** A 7B-class dense model evaluated across agentic, coding, long-context, and reasoning benchmarks. - **512K context.** Native 524,288-token context from the midtraining stages onward. - **Diffusion Adapters.** For faster inference (HF). - **Intermediate checkpoints.** Intermediate checkpoints are released so capability changes can be studied across training rather than at a single checkpoint. - **Fully open.** Training data and recipe, training code, and evaluation resources are public. The chart at the top of this card shows K2-Horizon-7B against selected reference models. The table below lists every comparison model used in the figure.
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View details ↗> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-7B for use with `llama.cpp`. > > Multiple precision and quantization variants are available, including `BF16`, `Q8_0`, `Q6_K`, `Q5_K_M`, `Q5_0`, and `Q4_K_M`. All GGUF files include tokenizer metadata and a llama.cpp-compatible chat template. > [!WARNING] > **💡 Important Deployment Notes:** These models require a version of `llama.cpp` containing K2 Horizon architecture support. Mainline support merged in release b11454. K2-Horizon-7B is the medium dense member of the K2-Horizon family: a 7B-core decoder-only model with a 512K context window.
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View details ↗K2-Horizon-MoVA-36B-A4B is the sparse member of the K2-Horizon family: a Mixture-of-Experts model with Mixture-of-Values attention (MoVA) that stores 36B parameters and runs 4B per token. We have released the checkpoints, along with the data and the training code. Open-weight models K2-Horizon-MoVA-36B-A4B Nemotron 3 Ultra Nemotron 3 Super G9v3-39A5B Qwen3.6-35B-A3B Muse Glimmer-30B Gemma 4 31B-it # Params 36B 550B 120B 39B 35B 30B 31B # Activated params 4B 55B 12B 5B 3B 30B 31B Architecture MoE MoE MoE MoE MoE Dense Dense Agents tau3-Banking Agentic tool use 26.8 14.2 10.3 22.1 9.3 23.5 14.8 Coding Terminal-Bench 2.1 Agentic terminal use 58.6 53.9 38.6 32.6 44.9 51.7 43.4 SciCode Scientific coding 38.9 39.9 36.0 34.0 35.8 43.6 43.4 Scientific Reasoning Humanity's Last Exam (without tools) Expert-level reasoning 25.2 28.4 20.8 17.5 22.2 22.0 23.6 GPQA Diamond Graduate-level science QA 80.8 86.7 80.0 80.5 84.1 83.5 85.7 CritPt Frontier physics reasoning 2.1 3.1 3.1 0.3 0.3 2.6 1.4 General AA-LCR Long-context reasoning 66.3 71.0 60.3 62.0 66.7 80.0 68.3 AA-Omniscience Accuracy Factual accuracy 18.8 22.6 24.3 14.9 18.8 27.0 20.0 AA-Omniscience Non-Hallucination Non-hallucination rate 69.2 70.3 13.0 87.0 49.5 18.1 15.0 Scores in %. Bold marks the best score in each row. Sections follow the Artificial Analysis Intelligence Index categories. Baseline scores are from Artificial Analysis; Muse Glimmer-30B at high reasoning effort, all other open models in their reasoning mode.
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View details ↗> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-MoVA-36B-A4B for use with `llama.cpp`. > > Multiple precision and quantization variants are available, including `BF16`, `Q8_0`, `Q6_K`, `Q5_K_M`, `Q5_0`, and `Q4_K_M`. All GGUF files include tokenizer metadata and a llama.cpp-compatible chat template. > [!WARNING] > **💡 Important Deployment Notes:** These models require a version of `llama.cpp` containing K2 Horizon architecture support. Mainline support merged in release b11454. K2-Horizon-MoVA-36B-A4B is the sparse member of the K2-Horizon family: a Mixture-of-Experts model with Mixture-of-Values attention (MoVA) that stores 36B parameters and runs 4B per token. We have released the final checkpoint; intermediate checkpoints, along with the data and the training code, will be released.
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View details ↗K2-Think
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View details ↗K2-Think-V2
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View details ↗K2-Type-0.9B
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