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Meta Muse Spark 1.3 is a multimodal reasoning model from Meta that is especially good at agentic and coding workflows, including longer-horizon work across multiple steps.
Muse Spark 1.3 takes text, image, and video input and returns text or tool calls through the Meta Model API.
meta/muse-spark-1.3https://api.aimlapi.com/v1Muse Spark 1.3 is a Meta model available today in Muse Code and in the Meta Model API.
Meta describes it as part of its latest rollout for coding and agentic workflows.
| Developer | Meta |
|---|---|
| Model ID | meta/muse-spark-1.3 |
| Release Date | September 2, 2026 |
| Context Window | 1,048,576 tokens (~1.0M) |
| Modalities | Image, Text → Text |
| Capabilities | file input, parallel tool calls, reasoning, streaming, structured (JSON schema) output, tool/function calling, vision, web search |
Availability
Muse Spark 1.3 is rolling out in Muse Code and the Meta Model API.
Launch timing
Meta’s announcement for Muse Spark 1.3 was published on September 2, 2026.
Product positioning
The model is presented as Meta’s newest update in its Muse Spark line.
Reported focus
Coverage around the release says the update improves coding and agentic work performance.
Coding workflows
Muse Spark 1.3 is available in Muse Code, indicating use in coding-focused workflows.
API-based integration
The model is also available through the Meta Model API for developer integration.
Sources: https://research.meta.ai/blog/introducing-muse-spark-1-3 · https://www.datacamp.com/blog/muse-spark-1-3 · https://codersera.com/blog/muse-spark-complete-guide-2026/
Muse Spark 1.3 is documented as suited for agentic workflows, including multi-step tool use, browser-based tasks, and long-horizon work in a single thread. It is also positioned for software engineering and coding assistance, with improved coding performance and long-running coding tasks across repositories.
It is documented for use with chat completion, image understanding, video and audio understanding, tool calling, structured output, and search grounding. It is also described as useful for web research and automation, professional and computer-use agents, and long-context retrieval.
See how Muse Spark 1.3 compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.
Compare 6 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $1.32 | 1M tokens |
| Output | $4.5 | 1M tokens |
| Cached input | $0.1587 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $1.32 | $4.5 | |
| $0.3703 | $1.59 | |
| $1.32 | $4.5 | |
| $1.32 | $4.5 | |
| $4.23 | $21.16 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "meta/muse-spark-1.3",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "meta/muse-spark-1.3",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "meta/muse-spark-1.3",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Muse Spark 1.3 has a 1,048,576 tokens context window.
Muse Spark 1.3 takes image, text as input and returns text.
Use meta/muse-spark-1.3 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Muse Spark 1.3 became available on September 2, 2026.
Muse Spark 1.3 costs $1.32 input and $4.5 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Yes, Muse Spark 1.3 can stream responses as they are generated.
Yes, Muse Spark 1.3 accepts image input alongside text.
Muse Spark 1.3 was built by Meta.
Yes, it supports function calling along with parallel tool calls and structured outputs.