Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)

Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)を、知能、料金、速度、コンテキストウィンドウなどで比較します。

方法論の詳細は、方法論のページをご覧ください。

ハイライト

知能

Artificial Analysis Intelligence Index · Higher is better

速度

Output tokens per second · Higher is better

タスクあたりの費用

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

モデル比較

MetaのロゴMeta
Muse Spark 1.3 (xhigh)
AnthropicのロゴAnthropic
Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)
Intelligence Index
61
58
Muse Spark 1.3 (xhigh)はClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)より知能が高いです
100万トークンあたりの料金
$0.78
$7.17
Muse Spark 1.3 (xhigh)はClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)より安価です
出力速度
235トークン/秒
59トークン/秒
Muse Spark 1.3 (xhigh)はClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)より高速です
最初のトークンまでの時間
20.77秒
6.46秒
Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)はMuse Spark 1.3 (xhigh)より速く応答します
コンテキストウィンドウ
1000kトークンArial 12ポイントのA4用紙約1,500ページ分
1000kトークンArial 12ポイントのA4用紙約1,500ページ分
Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)のコンテキストウィンドウは同じ規模です
リリース日
2026年9月
2026年9月
Muse Spark 1.3 (xhigh)はClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)より最近リリースされました
推論
はい
はい
Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)はどちらも推論に対応しています
画像入力への対応
はい
はい
Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)はどちらも画像入力に対応しています
オープンウェイト
いいえ
いいえ
Muse Spark 1.3 (xhigh)とClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)はどちらも独自モデルです

知能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR

Artificial Analysis Intelligence Index v4.1.1 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 v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR

Artificial Analysis Intelligence Index v4.1.1 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 commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

ベンチマーク

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
See more
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

No data available
Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

No data available
AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

No data available
IFBench

Instruction following

No data available
APEX-Agents-AA

Long-horizon agentic tasks

No data available
ITBench-AA

Kubernetes incident root-cause analysis

No data available
MMMU-Pro

Visual reasoning

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

Artificial Analysis Intelligence Index v4.1.1 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

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.

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.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
No data available

Intelligence Indexの比較

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

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 v4.1.1 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.

トークン使用量

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

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).

費用

Cost per Intelligence Index Task

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

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

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.

コンテキストウィンドウ

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

出力速度（1秒あたりのトークン数）で測定

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.

遅延

最初のトークンまでの時間（秒）で測定

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.

エンドツーエンド応答時間

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).

モデル規模（オープンウェイトモデルのみ）

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
No data available

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.

よくある質問

Muse Spark 1.3 (xhigh)の知能が上です。Artificial Analysis Intelligence Indexで、Muse Spark 1.3 (xhigh)のスコアは61、Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)は58です。

Muse Spark 1.3 (xhigh)の方が高速です。出力速度はMuse Spark 1.3 (xhigh)が毎秒235.2トークン、Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)が毎秒59.1トークンです。

Muse Spark 1.3 (xhigh)の方が安価です。100万トークンあたりの料金はMuse Spark 1.3 (xhigh)が$0.78、Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)が$7.17です（キャッシュヒット／入力／出力を7:2:1とした場合）。

Claude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)の方が遅延が短くなっています。最初のトークンまでの時間はClaude Fable 5.1 (Adaptive Reasoning, Low Effort, Default Fallback)が6.46秒、Muse Spark 1.3 (xhigh)が20.77秒です。

両モデルのコンテキストウィンドウは1.0Mトークンです。