此模型已弃用。我们仅继续对默认的 10k 输入 token 工作负载进行性能测试。其他工作负载的结果为历史数据，不再更新。

Sarvam 已推出更新版本 Sarvam 30B。我们建议考虑使用该版本。

Sarvam

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Open weights model

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Released 2025年5月

Sarvam M (Reasoning, Based on Mistral Small 3.1) 智能、性能与价格分析

立即试用 API 服务商基准测试

智能

Artificial Analysis Intelligence Index

* 估计值

速度

无

每秒输出 token 数

成本

输入 US$0.00输出 US$0.00

无

Intelligence Index 每项任务的成本

冗长度

无

Intelligence Index 输出 token 数

Sarvam M (Reasoning, Based on Mistral Small 3.1) 的智能水平在同类模型中处于末段，但价格很有竞争力；比较对象为其他规模相近的开放权重模型。该模型支持文本输入，可输出文本，上下文窗口为 33k 个 token。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 在 Artificial Analysis Intelligence Index 上的得分为 5，在同类模型中处于末段（中位数：8）。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 每 100 万输入 token 的价格为 $0.00（很有竞争力，中位数：$0.12），每 100 万输出 token 的价格为 $0.00（很有竞争力，中位数：$0.25）。

推理	是此页面展示该模型的推理版本。可能还存在非推理版本。
输入模态	支持：文本
输出模态	支持：文本
上下文窗口	33k 约 49 页 A4 纸（12 号 Arial 字体）
总参数量	23.6B
许可证	Apache 2.0
模型权重	Hugging Face

指标与同类别模型进行比较：

非推理模型 → 仅与其他非推理模型比较
推理模型 → 同时与推理和非推理模型比较
开放权重模型 → 仅与规模类别相同的其他开放权重模型比较：

微型：≤4B 个参数
小型：4B–40B 个参数
中型：40B–150B 个参数
大型：>150B 个参数

专有模型 → 与价格区间相同的专有模型和开放权重模型比较，采用输入/输出价格 3:1 的混合比例：

每 100 万 token <$0.15
每 100 万 token $0.15–$1
每 100 万 token >$1

智能

Artificial Analysis Intelligence Index · Higher is better

速度

Output tokens per second · Higher is better

每任务成本

Weighted average cost (USD) per Intelligence Index task · Lower is better

智能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

No publicly available API

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index：开放权重与专有模型

No publicly available API

Estimate (independent evaluation forthcoming)

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index 与每项任务的成本

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

基准测试

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

No publicly available API

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

AA-LCR v1.1

Long context reasoning

SciCodeUnder review

Coding

CritPtUnder review

Physics reasoning

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AA

Medical long context reasoning

MMMU-Pro

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

No publicly available API

Openness Index

Artificial Analysis Openness Index：得分

Openness Index 以 0 至 100 的标准化评分衡量模型的开放程度（分数越高，越开放）

Intelligence Index 比较

Intelligence Index 与每项任务的成本

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

成本

每项 Intelligence Index 任务的成本

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

运行 Artificial Analysis Intelligence Index 的成本

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

价格：缓存命中、输入和输出

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

上下文窗口

Context window: tokens limit · Higher is better

No publicly available API

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

模型规模（仅开放权重模型）

模型规模：总参数量与活跃参数量

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

常见问题

关于 Sarvam M (Reasoning, Based on Mistral Small 3.1) 的常见问题

Sarvam M (Reasoning, Based on Mistral Small 3.1) 发布于 2025年5月23日。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 由 Sarvam 开发。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 在 Artificial Analysis Intelligence Index 上的估算得分为 5，在其他规模相近的开放权重模型中处于末段（中位数：8）。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 每 100 万输入 token 的价格为 $0.00（非常有竞争力，中位数：$0.12），每 100 万输出 token 的价格为 $0.00（非常有竞争力，中位数：$0.25）；基于提供该模型的各服务商中位数。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 每 100 万输入 token 的价格为 $0.00，每 100 万输出 token 的价格为 $0.00（基于提供该模型的各服务商中位数）。按缓存命中/输入/输出为 7:2:1 的比例计算，混合价格为每 100 万 token $0.00。价格可能因服务商而异。比较服务商价格

是，Sarvam M (Reasoning, Based on Mistral Small 3.1) 是推理模型。它会在给出答案之前，通过扩展思考或思维链推理来解决复杂问题。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 支持文本输入。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 支持文本输出。

否，Sarvam M (Reasoning, Based on Mistral Small 3.1) 不支持图像输入，只能处理文本。

否，Sarvam M (Reasoning, Based on Mistral Small 3.1) 不是多模态模型，仅支持文本输入。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 的上下文窗口为 33k 个 token。这决定了模型在单次请求中可以处理多少文本和对话历史。

是，Sarvam M (Reasoning, Based on Mistral Small 3.1) 是开放权重模型，其模型权重已公开，可供下载并自行托管。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 有 23.6B 参数。

Sarvam M (Reasoning, Based on Mistral Small 3.1) 基于 Apache 2.0 许可证发布，该许可证允许商业使用。查看许可证

Sarvam M (Reasoning, Based on Mistral Small 3.1) 在 Artificial Analysis Intelligence Index 上的得分为 5。这项综合基准测试评估模型的推理、知识、数学和编程能力。

是，可通过 1 家服务商的 API 使用 Sarvam M (Reasoning, Based on Mistral Small 3.1)。比较 API 服务商

可通过 1 家 API 服务商使用 Sarvam M (Reasoning, Based on Mistral Small 3.1)。比较服务商

Sarvam M (Reasoning, Based on Mistral Small 3.1) 智能、性能与价格分析

模型摘要

智能

速度

成本

冗长度

比较摘要

技术规格

此类别中有 142 个模型

智能

速度

每任务成本

智能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index：开放权重与专有模型

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index 与每项任务的成本

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

基准测试

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index：得分

Intelligence Index 比较

Intelligence Index 与每项任务的成本

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

成本

每项 Intelligence Index 任务的成本

Cost per Intelligence Index Task

运行 Artificial Analysis Intelligence Index 的成本

Cost to Run Artificial Analysis Intelligence Index

价格：缓存命中、输入和输出

Cache Hit

上下文窗口

上下文窗口

Context Window for RAG

Context Window

模型规模（仅开放权重模型）

模型规模：总参数量与活跃参数量

Total Parameters

Active Parameters at Inference Time

常见问题

Sarvam M (Reasoning, Based on Mistral Small 3.1) 何时发布？

谁开发了 Sarvam M (Reasoning, Based on Mistral Small 3.1)？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 有多智能？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 的费用是多少？

Sarvam M (Reasoning, Based on Mistral Small 3.1) API 的价格是多少？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 是推理模型吗？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 支持哪些输入模态？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 支持哪些输出模态？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 能处理图像吗？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 是多模态模型吗？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 的上下文窗口有多大？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 是开放权重模型吗？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 有多少个参数？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 使用什么许可证？

Sarvam M (Reasoning, Based on Mistral Small 3.1) 在基准测试中的表现如何？

可以通过 API 使用 Sarvam M (Reasoning, Based on Mistral Small 3.1) 吗？

可以在哪里使用 Sarvam M (Reasoning, Based on Mistral Small 3.1)？