K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window. The chart at the top of this card shows K2-Horizon-0.9B against selected reference models. The table below lists every comparison model used in the figure. Reference models K2-Horizon-0.9B Qwen3.5-0.8B OpenBMB-1B Qwen3.5-2B # Params 0.9B 0.8B 1B 2B # Activated params 0.9B 0.8B 1B 2B Architecture Dense Dense Dense Dense Math AIME 2025 Competition mathematics 41.7 1.0 40.4 34.2 AIME 2026 Competition mathematics 48.5 0.2 40.4 38.8 HMMT Feb 2026 Competition mathematics 25.8 0.6 23.3 22.7 Scientific Reasoning GPQA Diamond Graduate-level science QA 27.3 11.9 26.3 54.9 Coding HumanEval+ Code generation 79.9 16.5 65.2 75.6 MBPP+ Code generation 68.0 35.4 60.6 67.7 LiveCodeBench v6 Competitive coding 37.4 6.6 33.5 29.8 Agents BFCL v4 Function calling 28.0 25.3 25.2 43.6

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K2-Horizon-0.9B-GGUF

IFM

> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-0.9B 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-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window.

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K2-Horizon-3.7B

IFM

K2-Horizon-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window. The chart at the top of this card shows K2-Horizon-3.7B against selected reference models. The table below lists every comparison model used in the figure. Open-weight dense models K2-Horizon-3.7B Qwen3.5-4B G9v3-3B Granite 4.2-3B Nemotron 3 Nano-4B # Params 3.7B 4B 3B 3B 4B # Activated params 3.7B 4B 3B 3B 4B Architecture Dense Dense Dense Dense Dense Math HMMT Feb 2026 Competition mathematics 75.2 61.6 34.1 57.2 34.7 Coding SWE-bench Verified Software engineering 65.6 41.2 16.4 32.2 1.8 Scientific Reasoning GPQA Diamond Graduate-level science QA 68.9 77.1 43.8 55.9 51.3 HLE Expert-level reasoning 14.0 9.9 4.5 6.6 4.9 Coding SciCode Scientific coding 25.8 16.1 17.7 24.9 16.4 Terminal-Bench 2.1 Agentic terminal use 39.7 25.8 6.0 13.9 3.7 Agents tau3-Banking Agentic tool use 17.7 6.8 — 5.6 — BFCL v4 Function calling 64.6 55.7 47.9 50.8 36.8

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K2-Horizon-3.7B-GGUF

IFM

> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-3.7B 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-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window.

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K2-Horizon-32B

IFM

K2-Horizon-32B is the large dense member of the K2-Horizon family: a 32B decoder-only model with a 512K context window. Open-weight dense models K2-Horizon-32B Qwen3.8-27B Muse Glimmer-30B IBM Granite 4.2 30B # Params 32B 27B 30B 30B # Activated params 32B 27B 30B 30B Architecture Dense Dense Dense Dense Agents tau3-Banking Agentic tool use 24.1 48.0 23.5 14.4 Coding Terminal-Bench 2.1 Agentic terminal use 62.6 79.8 51.7 26.6 SciCode Scientific coding 37.9 44.7 43.6 36.6 Scientific Reasoning Humanity's Last Exam (without tools) Expert-level reasoning 25.2 33.9 22.0 11.2 GPQA Diamond Graduate-level science QA 82.8 90.5 83.5 64.4 CritPt Frontier physics reasoning 2.9 5.4 2.6 0.3 General AA-LCR Long-context reasoning 68.0 77.3 80.0 46.7 AA-Omniscience Accuracy Factual accuracy 18.2 15.6 27.0 10.1 AA-Omniscience Non-Hallucination Non-hallucination rate 69.7 69.7 18.1 74.4 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, other open models in their reasoning mode.

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K2-Horizon-32B-FP8

IFM

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 58

Amber

Crystal

CrystalChat

Jais-3-0.9B-GGUF

Jais-3-0.9B-MLX-bf16

Jais-3-3.7B-GGUF

Jais-3-3.7B-MLX-bf16

Jais-3-32B-GGUF

Jais-3-32B-MLX-bf16

Jais-3-7B-GGUF

Jais-3-7B-MLX-bf16

Jais-3-MoVA-36B-A4B-FP8

Jais-3-MoVA-36B-A4B-GGUF

Jais-3-MoVA-36B-A4B-MLX-bf16

Jais-ASR

Jais-ASR-FP8

Jais-ASR-GGUF

K2

K2-Horizon-0.9B

K2-Horizon-0.9B-GGUF

K2-Horizon-3.7B

K2-Horizon-3.7B-GGUF

K2-Horizon-32B

K2-Horizon-32B-FP8

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。