AI21-Jamba-Mini-1.5
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AI21-Jamba-Reasoning-3B
ai21labsAI21’s Jamba Reasoning 3B is a top-performing reasoning model that packs leading scores on intelligence benchmarks and highly-efficient processing into a compact 3B build. Read the full blog post here. The hybrid design combines Transformer attention with Mamba (a state-space model). Mamba layers are more efficient for sequence processing, while attention layers capture complex dependencies. This mix reduces memory overhead, improves throughput, and makes the model run smoothly on laptops, GPUs, and even mobile devices, while maintainig impressive quality. **Smart: Leading intelligence scores** The model outperforms competitors, such as Gemma 3 4B, Llama 3.2 3B, and Granite 4.0 Micro, on a combined intelligence score that averages 6 standard benchmarks.
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查看详情 ↗AI21-Jamba-Reasoning-3B-GGUF
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查看详情 ↗AI21-Jamba2-3B
ai21labsJamba2 3B is an ultra-compact open source model designed to bring enterprise-grade reliability to on-device deployments. At just 3B parameters, it runs efficiently on consumer devices—iPhones, Androids, Macs, and PCs—while maintaining the grounding and instruction-following capabilities required for production use. Released under Apache 2.0 License with a 256K context window, Jamba2 3B enables developers to build reliable AI applications for edge environments. For more details, read the full release blog post. * **On-device deployment:** Runs efficiently on iPhones, Androids, Macs, and PCs * **Ultra-compact footprint:** 3B parameters enabling edge deployments with minimal resources * **Benchmark leadership:** Excels on IFBench, IFEval, Collie, and FACTS * **256K context window:** Processes long documents and knowledge bases * **Apache 2.0 License:** Fully open source for commercial use * **SSM-Transformer architecture:** Memory-efficient design for resource-constrained environments
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查看详情 ↗AI21-Jamba2-Mini
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查看详情 ↗AI21-Jamba2-Mini-FP8
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查看详情 ↗Jamba-tiny-dev
ai21labsThis is a tiny Jamba model used for development, debugging and experimentation over the Jamba architecture. It has 319M parameters (instead of 52B in Jamba 1.5 Mini (and Jamba v0.1) and 398B in Jamba 1.5 Large), and was trained on ~40B tokens. It is great for use in unittests since it is a small model (doesn't take long to download) that has valid and non-random outputs. Yet, **it did not undergo extensive training and should not be expected to generate high-quality text**.
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查看详情 ↗Jamba-tiny-random
ai21labsThis is a tiny, dummy version of Jamba, used for debugging and experimentation over the Jamba architecture. It has 128M parameters (instead of 52B), **and is initialized with random weights and did not undergo any training.**
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查看详情 ↗Jamba-tiny-reward-dev
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查看详情 ↗Jamba-v0.1
ai21labsThis is the base version of the Jamba model. We’ve since released a better, instruct-tuned version, Jamba-1.5-Mini. For even greater performance, check out the scaled-up Jamba-1.5-Large. Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks. Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.
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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。