InclusionAI

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Open weights model

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Released 2026年8月

Ling 3.0 Flash 智能、性能与价格分析

立即试用 API 服务商基准测试

智能Updated

Artificial Analysis Intelligence Index

成本

输入 US$0.075输出 US$0.22缓存折扣 80%

无

Intelligence Index 每项任务的成本

冗长度

无

Intelligence Index 输出 token 数

Ling 3.0 Flash 的智能水平处于领先行列，价格也较为合理；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 262k 个 token。

Ling 3.0 Flash 在 Artificial Analysis Intelligence Index 上的得分为 25，在同类模型中远高于平均水平（中位数：8）。

Ling 3.0 Flash 每 100 万输入 token 的价格为 $0.07（中等，中位数：$0.14），每 100 万输出 token 的价格为 $0.22（中等，中位数：$0.40）。

Ling 3.0 Flash 的速度为每秒 276 个 token，明显较快（128）。

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	262k 约 393 页 A4 纸（12 号 Arial 字体）
总参数量	124B
活跃参数量	5.1B 推理期间每个 token 启用的参数数量

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

亮点

Updated

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Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, 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 by Open Weights / Proprietary

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.

基准测试

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

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

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench v4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

𝜏³-Banking

Agentic tool use

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

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

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

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.

开放性指数

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index 比较

Intelligence Index vs. Cost per Intelligence Index Task

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.

成本

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

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).

Pricing: Cache Hit, Input, and Output

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

Context window: tokens limit · Higher is better

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).

速度

按输出速度（每秒 token 数）衡量

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

延迟

按首 Token 延迟（秒）衡量

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

端到端响应时间

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

模型规模（仅开放权重模型）

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

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 Ling 3.0 Flash 的常见问题

Ling 3.0 Flash 发布于 2026年8月4日。

Ling 3.0 Flash 由 InclusionAI 开发。

Ling 3.0 Flash 在 Artificial Analysis Intelligence Index 上的估算得分为 25，在其他规模相近的开放权重模型中远高于平均水平（中位数：8）。

Ling 3.0 Flash 以每秒 275.9 个 token 的速度生成输出（基于 InclusionAI 的 API），与其他规模相近的开放权重模型相比远高于平均水平（中位数：128.2 t/s）。

Ling 3.0 Flash 的首 Token 延迟（TTFT）为 2.65 秒（基于 InclusionAI 的 API），与其他规模相近的开放权重模型相比略高于平均水平（中位数：2.29 秒）。

Ling 3.0 Flash 每 100 万输入 token 的价格为 $0.07（非常有竞争力，中位数：$0.38），每 100 万输出 token 的价格为 $0.22（非常有竞争力，中位数：$0.71）；基于 InclusionAI 的 API。

Ling 3.0 Flash 每 100 万输入 token 的价格为 $0.07，每 100 万输出 token 的价格为 $0.22（基于 InclusionAI 的 API）。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 $0.05。价格可能因服务商而异。比较服务商价格

是，Ling 3.0 Flash 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。

Ling 3.0 Flash 支持文本输入。

Ling 3.0 Flash 支持文本输出。

否，Ling 3.0 Flash 不支持图像输入，只能处理文本。

否，Ling 3.0 Flash 不是多模态模型，仅支持文本输入。

Ling 3.0 Flash 的上下文窗口为 260k 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，Ling 3.0 Flash 是开放权重模型，其模型权重已公开。

Ling 3.0 Flash 有 124B 参数（启用 5.1B）。

Ling 3.0 Flash 是混合专家（MoE）模型，总参数量为 124B，但推理期间仅启用 5.1B。

Ling 3.0 Flash 在 Artificial Analysis Intelligence Index 上的得分为 25。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 2 家服务商的 API 使用 Ling 3.0 Flash。比较 API 服务商

可通过 2 家 API 服务商使用 Ling 3.0 Flash。比较服务商

Ling 3.0 Flash 智能、性能与价格分析

模型摘要

智能Updated

速度

成本

冗长度

比较摘要

技术规格

此类别中有 64 个模型

智能

速度

每任务成本

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

基准测试

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

开放性指数

Artificial Analysis Openness Index: Score

Intelligence Index 比较

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

成本

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

上下文窗口

Context Window

Context Window for RAG

Context Window

速度

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

延迟

Latency: Time To First Answer Token

Time to First Answer Token

端到端响应时间

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

模型规模（仅开放权重模型）

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

常见问题

Ling 3.0 Flash 何时发布？

谁开发了 Ling 3.0 Flash？

Ling 3.0 Flash 有多智能？

Ling 3.0 Flash 有多快？

Ling 3.0 Flash 的延迟是多少？

Ling 3.0 Flash 的费用是多少？

Ling 3.0 Flash API 的价格是多少？

Ling 3.0 Flash 是推理模型吗？

Ling 3.0 Flash 支持哪些输入模态？

Ling 3.0 Flash 支持哪些输出模态？

Ling 3.0 Flash 能处理图像吗？

Ling 3.0 Flash 是多模态模型吗？

Ling 3.0 Flash 的上下文窗口有多大？

Ling 3.0 Flash 是开放权重模型吗？

Ling 3.0 Flash 有多少个参数？

Ling 3.0 Flash 有多少活跃参数？

Ling 3.0 Flash 在基准测试中的表现如何？