Mistral-Small-4-119B-2603-NVFP4
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探索 AI 模型 118
Mistral Small 4 (Non-reasoning)
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查看详情 ↗Mistral-Small-Instruct-2409
Mistral AIWe recommend using this model with the vLLM library to implement production-ready inference pipelines. If you want to divide the GPU requirement over multiple devices, please add *e.g.* `--tensor_parallel=2` After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
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查看详情 ↗Mixtral 8x22B Instruct
Mistral AIMistral's official instruct fine-tuned version of Mixtral 8x22B. It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...
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$2 / M 输入 TokenOpenRouter · mistralai/mixtral-8x22b-instruct
查看详情 ↗Mixtral-8x22B-v0.1
Mistral AIThe Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. For full details of this model please read our release blog post. This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different.
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查看详情 ↗Mixtral 8x7B Instruct
Mistral AI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 ↗Mixtral-8x7B-Instruct-v0.1
Mistral AI> [!TIP] > PRs to correct the transformers tokenizer so that it gives 1-to-1 the same results as the mistral-common reference implementation are very welcome! --- The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
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查看详情 ↗Mixtral-8x7B-v0.1
Mistral AIThe Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our release blog post. This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
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查看详情 ↗Pixtral-12B-2409
Mistral AIThe performance of Pixtral-12B-2409 compared to multimodal models. All models were re-evaluated and benchmarked through the same evaluation pipeline. We recommend using Pixtral with the vLLM library to implement production-ready inference pipelines with Pixtral. After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can pass text and images or image urls to the model in *instruction-following* mode as follows:
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查看详情 ↗Pixtral-12B-Base-2409
Mistral AI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 ↗Pixtral Large
Mistral AI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 ↗Pixtral-Large-Instruct-2411
Mistral AI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 ↗Shieldstral-1.0-3B
Mistral AI**Shieldstral** is a compact **3B-parameter, policy-adaptive multimodal safety classifier**. Instead of predicting a fixed set of moderation categories, Shieldstral evaluates content against a safety policy expressed in **natural language** and returns a single continuous safety score. This makes it a flexible drop-in guardrail for text-only, image-only, and text+image moderation that can be re-targeted to new policies **at inference time, without retraining**. It is built on Ministral-3-3B-Base-2512 with a native Pixtral vision encoder, and produces its verdict from a **single forward pass**. Learn more in our blog post and technical report.
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查看详情 ↗Timestral-123M
Mistral AITimestral is a **family of time series foundation models** for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 123M checkpoint. >[!Note] > For more information about our Timestral family, go to the main model page: Timestral-209M. Timestral-209M achieves **state-of-the-art zero-shot accuracy** across three major forecasting benchmarks: GIFT-Eval, TIME, and fev-bench.
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查看详情 ↗Timestral-209M
Mistral AITimestral is a **family of time series foundation models** for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 209M checkpoint. Timestral supports univariate and multivariate forecasting, past-only and known-future covariates, and probabilistic predictions across 21 quantiles. It accepts up to 32,768 historical time steps and forecasts up to 1,024 steps in a single forward pass. We’re releasing the source code in our GitHub repository to make it easy to run Timestral and reproduce our benchmark results.
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查看详情 ↗Timestral-28M
Mistral AITimestral is a **family of time series foundation models** for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 28M checkpoint. >[!Note] > For more information about our Timestral family, go to the main model page: Timestral-209M. Timestral-209M achieves **state-of-the-art zero-shot accuracy** across three major forecasting benchmarks: GIFT-Eval, TIME, and fev-bench.
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查看详情 ↗Timestral-5M
Mistral AITimestral is a **family of time series foundation models** for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 5M checkpoint. >[!Note] > For more information about our Timestral family, go to the main model page: Timestral-209M. Timestral-209M achieves **state-of-the-art zero-shot accuracy** across three major forecasting benchmarks: GIFT-Eval, TIME, and fev-bench.
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查看详情 ↗Voxtral-4B-TTS-2603
Mistral AI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 ↗Voxtral-Mini-3B-2507
Mistral AIVoxtral Mini is an enhancement of Ministral 3B, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Learn more about Voxtral in our blog post here and our research paper. Voxtral builds upon Ministral-3B with powerful audio understanding capabilities. - **Dedicated transcription mode**: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, Voxtral automatically predicts the source audio language and transcribes the text accordingly - **Long-form context**: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding - **Built-in Q&A and summarization**: Supports asking questions directly through audio. Analyze audio and generate structured summaries without the need for separate ASR and language models - **Natively multilingual**: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian) - **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents - **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Ministral-3B
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查看详情 ↗Voxtral-Mini-4B-Realtime-2602
Mistral AIVoxtral Mini 4B Realtime 2602 is a **multilingual, realtime speech-transcription model** and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of ** = 3600 / 0.8 = 45000`. In theory, you should be able to record with no limit; in practice, pre-allocations of RoPE parameters among other things limits `--max-model-len`. For the best user experience, we recommend to simply instantiate vLLM with the default parameters which will automatically set a maximum model length of 131072 (~ca. 3h). - We strongly recommend using websockets to set up audio streaming sessions. For more info on how to do so, check Usage. - We recommend using a delay of 480ms as we found it to be the sweet spot of performance and low latency. If, however, you want to adapt the delay, you can change the `"transcription_delay_ms": 480` parameter in the tekken.json file to any multiple of 80ms between 80 and 1200, as well as 2400 as a standalone value. We compare Voxtral Mini 4B Realtime to similar models - both offline models and realtime. Voxtral Mini 4B Realtime is competitive to leading offline models and shows significant gains over existing open-source realtime solutions. > [!Warning] > Due to its novel architecture, Voxtral Realtime is currently only support in vLLM. We very much welcome community contributions > to add the architecture to Transformers and Llama.cpp.
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查看详情 ↗Voxtral-Mini-4B-Realtime-Arabic
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查看详情 ↗Voxtral Small 24B 2507
Mistral AIVoxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...
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$0.1 / M 输入 TokenOpenRouter · mistralai/voxtral-small-24b-2507
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。