OpenHathi-7B-Hi-v0.1-Base
sarvamai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。
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探索 AI 模型 12
sarvam-1
sarvamaiSarvam-1 is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages. More details can be found in our release blog. *Note: This is a text-completion model. It is meant to be finetuned on downstream tasks, and cannot be used directly as a chat or an instruction-following model.*
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查看详情 ↗sarvam-1-v0.5
sarvamai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。
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查看详情 ↗sarvam-105b
sarvamai1. Introduction 2. Architecture 3. Benchmarks - Knowledge & Coding - Reasoning & Math - Agentic 4. Inference - Hugging Face - vLLM - SGLang 5. Footnote 6. Citation **Sarvam-105B** is an advanced Mixture-of-Experts (MoE) model with 10.3B active parameters, designed for superior performance across a wide range of complex tasks. It is highly optimized for complex reasoning, with particular strength in agentic tasks, mathematics, and coding. Sarvam-105B is a top-tier performer, consistently matching or surpassing several major closed-source models and staying within a narrow margin of frontier models across diverse reasoning and agentic benchmarks. It demonstrates exceptional agentic and reasoning capabilities in real-world applications such as web search and technical troubleshooting.
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查看详情 ↗sarvam-105b-fp8
sarvamai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。
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查看详情 ↗sarvam-105b-gguf
sarvamai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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查看详情 ↗sarvam-30b
sarvamai1. Introduction 2. Architecture 3. Benchmarks - Knowledge & Coding - Reasoning & Math - Agentic 4. Inference - Hugging Face - vLLM - SGLang 5. Footnote 6. Citation **Sarvam-30B** is an advanced Mixture-of-Experts (MoE) model with 2.4B non-embedding active parameters, designed primarily for practical deployment. It combines strong reasoning, reliable coding ability, and best-in-class conversational quality across Indian languages. Sarvam-30B is built to run reliably in resource-constrained environments and can handle multilingual voice calls while performing tool calls. A major focus during training was the Indian context and languages, resulting in **state-of-the-art performance across 22 Indian languages** for its model size.
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查看详情 ↗sarvam-30b-fp8
sarvamai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。
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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 ↗sarvam-30b-gguf
sarvamai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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查看详情 ↗sarvam-m
sarvamai`sarvam-m` is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This post-trained version delivers exceptional improvements over the base model: Performance gains are even more impressive at the intersection of Indian languages and mathematics, with an outstanding +86% improvement in romanized Indian language GSM-8K benchmarks. > [!NOTE] > For thinking mode, we recommend `temperature=0.5`; for no-think mode, `temperature=0.2`.
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查看详情 ↗sarvam-m-q8-gguf
sarvamai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。
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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 ↗sarvam-translate
sarvamaiSarvam-Translate is an advanced translation model built by Sarvam AI in partnership with AI4Bharat, specifically designed for comprehensive, document-level translation across the 22 official Indian languages, built on Gemma3-4B-IT. It addresses modern translation needs by moving beyond isolated sentences to handle long-context inputs, diverse content types, and various formats. Sarvam-Translate aims to provide high-quality, contextually aware translations for Indian languages, which have traditionally lagged behind high-resource languages in LLM performance. `Assamese`, `Bengali`, `Bodo`, `Dogri`, `Gujarati`, `English`, `Hindi`, `Kannada`, `Kashmiri`, `Konkani`, `Maithili`, `Malayalam`, `Manipuri`, `Marathi`, `Nepali`, `Odia`, `Punjabi`, `Sanskrit`, `Santali`, `Sindhi`, `Tamil`, `Telugu`, `Urdu` The open-sourced checkpoint only supports: - English to Indian languages - Indian languages to English
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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。