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muse-spark-1.3Meta

from /1M in$1.25meta
context1.0M944K out
providers11 region
endpoints11 region
endpoints
1
regions
1
api
chat
released
October 2026

capabilities 6/8

visionreasoningtoolscachingweb searchjson schemacomputer useimage gen

endpoints

1 / 1 region
Providers serving muse-spark-1.3, with pricing and measured performance. Best value in each column is coloured.
#flags
1metaGlobal1.0M$1.25$4.25$0.15no-train
provider price, no markupper 1M tokensblank where no qualifying sample

measured

through Nov 2

no measured traffic for this model yet. the endpoints table above carries provider pricing

benchmarks

artificial analysis
Benchmark scores, each normalised to 100
evalScore as a share of 100/100
GPQA Diamond93.5
SciCode58.8
Humanity's Last Exam48.7
Intelligence Index36.4
4 evalsmean59.4

Scores from Artificial Analysis and public leaderboards, normalised to 100. Bar colour is the band, not the rank: green 80 and up, blue 55 and up, amber 30 and up. Benchmarks measure narrow skills, so test on your own workload before committing. Released 2026-09-02.

call it

model=
muse-spark-1.3

requesty 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

Base url is https://router.requesty.ai/v1 for every id here. One key reaches the whole catalog.

quickstart

openai compatible
main.py
from 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)

Change the base url, use your Requesty key, set the model to any id in the call pane. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog, so switching later is a one-parameter change. Browse all models

notes

reference

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.

questions 6

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 the cheapest provider. Prices vary by provider and region; the table above shows every endpoint. Requesty charges exactly what the upstream provider charges, with no markup.
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.

more from meta 6

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modelcontextfrom /Mproviders
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muse-wonder-1.01.0M$4.001
muse-glimmer-30b131Kfree2
llama-4-maverick-17b-128e-instruct1.0M$0.201
llama-3.3-70b-instruct131K$0.133
llama-3.3-70b-instruct-turbo131K$0.081

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. Weekly aggregates in the measured pane come from production traffic routed through Requesty, one line per provider on a shared axis. Methodology

muse-spark-1.31 provider1 endpointsfrom $1.25 /1M inctx 1.0Mupdated Nov 2