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Released April 2026

Muse Spark 智能、性能与价格分析

API 服务商基准测试

模型摘要

智能

43
Artificial Analysis Intelligence Index
智能为 4 档中的第 4 档。

速度

无
每秒输出 token 数
速度在 4 档中的档位未知。

输入价格

US$0.00
美元/100 万 token
输入价格为 4 档中的第 1 档。

输出价格

US$0.00
美元/100 万 token
输出价格为 4 档中的第 1 档。

冗长度

57M
Intelligence Index 输出 token 数
冗长度为 4 档中的第 2 档。

Muse Spark 的智能水平处于领先行列，价格也很有竞争力；比较对象为其他价格相近的模型。 该模型支持文本、图像和语音输入，可输出文本，上下文窗口为 262k 个 token。

Muse Spark 在 Artificial Analysis Intelligence Index 上的得分为 43，在同类模型中远高于平均水平（中位数：15）。在 Intelligence Index 评测中，它生成了 57M 个 token；与 63M 的中位数相比，其回答较为简洁。

Muse Spark 每 100 万输入 token 的价格为 $0.00（很有竞争力，中位数：$0.20），每 100 万输出 token 的价格为 $0.00（很有竞争力，中位数：$0.60）。

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、图像和语音

输出模态

支持：文本

上下文窗口262k
约 393 页 A4 纸（12 号 Arial 字体）

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

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 v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

基准测试

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

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

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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
Reasoning models are indicated by a lightbulb icon

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.
Reasoning models are indicated by a lightbulb icon

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.

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
Reasoning models are indicated by a lightbulb icon

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.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Token 使用量

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

成本

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

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.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

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)
Reasoning models are indicated by a lightbulb icon

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
Reasoning models are indicated by a lightbulb icon

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).

常见问题

关于 Muse Spark 的常见问题

Muse Spark 发布于 2026年4月8日。

Muse Spark 由 Meta 开发。

Muse Spark 在 Artificial Analysis Intelligence Index 上的得分为 43，在其他价格档位相近的推理模型中远高于平均水平（中位数：15）。

在 Intelligence Index 评测中，Muse Spark 生成了 57M 个输出 token；与其他价格档位相近的推理模型相比优于平均水平（中位数：63M）。

是，Muse Spark 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。

Muse Spark 支持文本、图像和语音输入。

Muse Spark 支持文本输出。

是，Muse Spark 支持图像输入，可以分析、描述图像并回答有关图像的问题。

是，Muse Spark 是多模态模型，可以处理文本、图像和语音输入并生成文本输出。

Muse Spark 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。

否，Muse Spark 是专有模型，其模型权重并未公开。

Muse Spark 是专有模型，Meta 尚未披露模型规模或参数量。

Muse Spark 在 Artificial Analysis Intelligence Index 上的得分为 43。这项综合基准测试评估模型的推理、知识、数学和编程能力。

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