This model is deprecated. We still benchmark its performance, but less often: every 18 hours instead of every 6.

Meta has launched a newer release, Muse Wonder. We suggest considering it instead.

Muse Spark 1.3: Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

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Performance

Time to first answer token 28.81sOutput speed 132 tok/s

11.5

Time per Intelligence Index task (minutes)

Cost

In $1.25Out $4.25Cache discount 88%

$2.39

Cost per Intelligence Index task

Verbosity

Input tokens 7.81BOutput/input 1.574%

123M

Output tokens from Intelligence Index

Muse Spark 1.3 (Max) is below average in intelligence, but well priced when comparing to other models of similar price. It's also quicker than average per task, however very verbose. The model supports text, image, and video input, outputs text, and has a 1M tokens context window.

Muse Spark 1.3 (Max) scores 37 on the Artificial Analysis Intelligence Index, placing it below average among comparable models (median: 41). When evaluating the Intelligence Index, it generated 123M tokens, which is very verbose in comparison to the median of 77.3M.

Pricing for Muse Spark 1.3 (Max) is $1.25 per 1M input tokens (competitively priced, median: $2.00) and $4.25 per 1M output tokens (competitively priced, median: $10.00). On average, it costs $2.39 per task to evaluate Muse Spark 1.3 (Max) on the Intelligence Index.

At 11.5 minutes per Intelligence Index task, Muse Spark 1.3 (Max) is quicker than average compared with the median of 13.1 minutes for comparable models (lower is better).

Reasoning	Yes
Input modality	Supports: text, image, and video
Output modality	Supports: text
Context window	1M ~1500 A4 pages of size 12 Arial font

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

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

No publicly available API

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. 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

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 vs. Cost per 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.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

Strategy & Ops Index

Incorporates 4 evaluations: AutomationBench-AA, AA-Briefcase v1.1, AA-Omniscience v1.1, GDP.pdf · Higher is better

Legal Index

Incorporates 5 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf, AutomationBench-AA · Higher is better

Healthcare & Medical Index

Incorporates 5 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, MLCR-AA, AutomationBench-AA, HLE-Diamond · Higher is better

Engineering Index

Incorporates 7 evaluations: AA-Omniscience v1.1, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1, HLE-Diamond, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Briefcase v1.1 · Higher is better

Economics Index

Incorporates 4 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf · Higher is better

Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode-AA 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

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

AA-PersonalityBenchNew

Personality Indexes

Each axis is one of 8 personality indexes: the mean of its traits’ Elo z-scores, percentile ranked from 1 to 100 across all models evaluated in the benchmark

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per 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.

Performance

Measured by time per task (minutes), output speed (tokens per second), time to first answer token and time to first token (seconds)

Time per Intelligence Index Task

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Speed at the model's own endpoint or the median across providers · Lower is better

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Output Speed

Output tokens divided by total request time, including time to first token · Higher is better

Output tokens, including reasoning tokens, received per second over a request's full duration, from sending the request to receiving the final token. The headline value averages each Performance v3 workload over the past 5 days, then averages the workloads.

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.

Token Use

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).

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).

Further details

								Weights
Muse Spark 1.3 (Max)	Meta	37	-	1M	$0.78	11.5	132	Not available
Muse Spark 1.3 (Xhigh)	Meta	-	-	1M	$0.78	-	218	Not available

Frequently Asked Questions

Common questions about Muse Spark 1.3

Muse Spark 1.3 was released on September 2, 2026.

Muse Spark 1.3 was created by Meta.

Muse Spark 1.3 has 2 effort levels: max and xhigh. Muse Spark 1.3 scores 37 (max) on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (Max) is below average among other models in a similar price tier (median: 41).

Muse Spark 1.3 takes 11.5 minutes (max) per Intelligence Index task (based on Meta's API; lower is better). Muse Spark 1.3 (Max) is quicker than average compared to other models in a similar price tier (median: 13.1 minutes).

Muse Spark 1.3 has a time to first answer token of 28.81s (max) and 16.84s (xhigh) (based on Meta's API). Muse Spark 1.3 (Max)'s time to first answer token is somewhat higher than average compared to other models in a similar price tier (median: 14.04s).

Muse Spark 1.3 costs $1.25 per 1M input tokens (very competitive, median: $2.00) and $4.25 per 1M output tokens (very competitive, median: $10.00), based on Meta's API.

Muse Spark 1.3 costs $1.25 per 1M input tokens and $4.25 per 1M output tokens (based on Meta's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.78 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Muse Spark 1.3 generated 123M output tokens (max). Muse Spark 1.3 (Max)'s output token count is at the higher end compared to other models in a similar price tier (median: 77.3M).

Yes, Muse Spark 1.3 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Muse Spark 1.3 supports text, image, and video input.

Muse Spark 1.3 supports text output.

Yes, Muse Spark 1.3 supports image input and can analyze, describe, and answer questions about images.

Yes, Muse Spark 1.3 is multimodal. It can process text, image, and video input and generate text output.

Muse Spark 1.3 has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.

No, Muse Spark 1.3 is proprietary. The model weights are not publicly available.

Muse Spark 1.3 is a proprietary model and Meta has not disclosed the model size or parameter count.

Muse Spark 1.3 achieves a score of 37 (max) on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

Yes, Muse Spark 1.3 is available via API through 1 provider. Compare API providers

Muse Spark 1.3 is available through 1 API provider. Compare providers

Muse Spark 1.3: Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Technical specifications

242 models in this class

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Capability Indexes

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-PersonalityBenchNew

Personality Indexes

Cost

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

Performance

Time per Intelligence Index Task

Time per Intelligence Index Task

Time Period

Output Speed

End-to-End Output Speed

Time to First Answer Token

Time to First Answer Token

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Context Window

Context Window

Context Window for RAG

Context Window

Further details

More from Meta

Frequently Asked Questions

When was Muse Spark 1.3 released?

Who created Muse Spark 1.3?

How intelligent is Muse Spark 1.3?

How fast is Muse Spark 1.3?

What is the latency of Muse Spark 1.3?

How much does Muse Spark 1.3 cost?

What is Muse Spark 1.3 API pricing?

How verbose is Muse Spark 1.3?

Is Muse Spark 1.3 a reasoning model?

What input modalities does Muse Spark 1.3 support?

What output modalities does Muse Spark 1.3 support?

Can Muse Spark 1.3 process images?

Is Muse Spark 1.3 multimodal?

What is the context window of Muse Spark 1.3?

Is Muse Spark 1.3 open source?

How many parameters does Muse Spark 1.3 have?

How does Muse Spark 1.3 perform on benchmarks?

Is Muse Spark 1.3 available via API?

Where can I use Muse Spark 1.3?