Jais-ASR
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
Jais-ASR-FP8
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查看详情 ↗K2
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查看详情 ↗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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查看详情 ↗> [!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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查看详情 ↗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. - **Strong small-model baseline.** A dense model evaluated on the same agentic, coding, and reasoning benchmarks as the rest of the family. - **512K context.** Native 524,288-token context from the midtraining stages onward. - **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-3.7B against selected reference models. The table below lists every comparison model used in the figure.
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查看详情 ↗> [!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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗> [!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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查看详情 ↗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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查看详情 ↗> [!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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查看详情 ↗K2-Think
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查看详情 ↗K2-Think-V2
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查看详情 ↗K2-Type-0.9B
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查看详情 ↗K2-V2
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
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
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。