muse-spark-1.3
Meta/Proprietary/1 provider
Muse Spark 1.3 is a reasoning model from Meta built for long horizon coding and multi step agentic work, with improved tool, browser and computer use, native tool calling and MCP support, and fewer tokens than 1.2. It accepts text, images, video and PDF documents with a 1M token context and returns text.MoreLess
Which id to call
muse-spark-1.3Requesty routes this id across every provider serving muse-spark-1.3, picking on price and health and failing over automatically. The id stays valid when a provider changes underneath it.
From /1M input
$1.25
meta
Context
1.0M
944K output
Endpoints
1
1 region
Regions
1
chat
Capabilities 6/8
1 endpoint / 1 region
Providers serving muse-spark-1.3
Provider prices, per 1M tokens. Pay as you go adds 5%, or 0% on your own keys.
| Rank | Privacy | ||||||
|---|---|---|---|---|---|---|---|
| 1 | meta | Global | 1.0M | $1.25 | $4.25 | $0.15 | no-train |
A column is blank where no qualifying sample exists, and every row links to that provider's endpoint page.
model=
Which id to call
One base url, https://router.requesty.ai/v1, and one key for every id here.
muse-spark-1.3Requesty routes this id across every provider serving muse-spark-1.3, picking on price and health and failing over automatically. The id stays valid when a provider changes underneath it.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="muse-spark-1.3", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Artificial Analysis
Benchmark scores
Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.
Graduate-level physics, chemistry & biology questions designed to resist Googling.
Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.
Scores from artificialanalysis.ai. They measure the model, not the provider, so they are the same on every endpoint above.
6
More from Meta
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1.0M ctx/from $4.00/1 provider
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131K ctx/from free/2 providers
llama-4-maverick-17b-128e-instruct
1.0M ctx/from $0.20/1 provider
llama-3.3-70b-instruct
131K ctx/from $0.13/3 providers
llama-3.3-70b-instruct-turbo
131K ctx/from $0.08/1 provider
llama-3.2-90b-vision-instruct
131K ctx/from $0.35/1 provider
Reference
muse-spark-1.3 questions
Which providers serve muse-spark-1.3?
muse-spark-1.3 is available from 1 provider through Requesty: meta. All endpoints share one OpenAI-compatible API and one key, and Requesty fails over between them automatically.
How much does muse-spark-1.3 cost?
Pricing starts at $1.25 per million input tokens and $4.25 per million output tokens on meta, the cheapest endpoint. Prices vary by provider and region; the table above shows every endpoint. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
Is muse-spark-1.3 open weights?
No. muse-spark-1.3 is a proprietary model from Meta, served through Meta's own API and licensed cloud platforms.
What is the context window of muse-spark-1.3?
muse-spark-1.3 supports up to 1.0M tokens of context, with up to 944K output tokens per response. Some providers expose a smaller window; check the per-endpoint context column above.
How do I use muse-spark-1.3 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "muse-spark-1.3" or any endpoint id from the table above. The quickstart section shows Python, JavaScript and cURL snippets.
Should I call muse-spark-1.3 by its managed id or a provider id?
Use the managed id, "muse-spark-1.3". Requesty picks which of the 1 provider serves each request based on price and health, and fails over when one degrades, so the id keeps working while the routing changes underneath it. Send a provider id like "meta/muse-spark-1.3" only when you need one specific deployment and want no failover.
Route muse-spark-1.3 through one endpoint
One key for 1 provider on this model and 600+ others. No markup on provider prices, automatic failover, caching built in.