Meta Llama: Muse Spark 1.3
meta/muse-spark-1.3
Try Muse Spark 1.3 for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "meta/muse-spark-1.3"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "meta/muse-spark-1.3"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Muse Spark 1.3 is Meta Superintelligence Labs' follow-up to the coding-focused Muse Spark 1.2, released on September 2, 2026. It targets long-horizon agentic work, multi-agent orchestration, and extended multi-step coding tasks.
On Meta's own harness it scored 88.8% on Terminal-Bench 2.1, level with OpenAI's GPT-5.6 Sol, and 75.4% on DeepSWE 1.1, up from 59.3% for 1.2. These are vendor-reported numbers, not yet independently verified. Artificial Analysis gives it an Intelligence Index score of 48, up from 40 for Muse Spark 1.2, and Anthropic's Opus 5 still leads it on several agent benchmarks.
It keeps the 1,048,576-token context window, text, image, video, and PDF input, and tool calling from earlier versions, at the same $1.25 per million input and $4.25 per million output token pricing.
Context Window 1M
tokens
Max Output 944K
tokens
Input Cost $1.25
per million tokens
Output Cost $4.25
per million tokens
Input text, image, video, file
modalities
Tool Use Yes
Release Date Sep 2, 2026
Output Speed 117
tokens / sec
Latency 36.96s
time to first token
Try Muse Spark 1.3 for free
Try Muse Spark 1.3 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Muse Spark 1.3 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 93.5% |
| Humanity's Last Exam Cross-domain reasoning | 48.7% |
| SciCode Scientific programming | 58.8% |
| LCR Long-context reasoning | 83.0% |
Scores sourced from Artificial Analysis.
Find other Meta Llama models →
Muse Wonder
Muse Wonder is a multimodal agent model from Meta Superintelligence Labs, built on Muse Spark and released on October 12, 2026. It accepts text, images, video, audio, and PDF input, returns text, and has a 1,048,576-token context window. It is designed for long, multi-step agent work such as coding, legal and financial documents, and tasks across several apps. It supports tool calling, structured output, and configurable reasoning effort. Meta reports 78.8 on DeepSWE 1.1 and 53.1 on SWE-Marathon, and Artificial Analysis gives it an Intelligence Index score of 52, up from 48 for Muse Spark 1.3. Its per-token price is below Claude Fable 5.1 ($10 input, $50 output), though Claude Opus 5.5 scores higher on the index and on OSWorld.
ChatMuse Glimmer 30B
Muse Glimmer 30B is a 30-billion-parameter dense model from Meta Superintelligence Labs, pairing a causal transformer with a 1.8-billion-parameter vision encoder for text and image input. It's released under an Apache 2.0 license, Meta's first fully open-weight model since Muse Spark moved to a paid API. On Meta's own benchmarks, it scores 76.0 on SWE-Bench Verified, 51.2 on SWE-Bench Pro, 94.7 on AIME 2026, 83.5 on GPQA Diamond, 75.5 on MCP Atlas, and 74.6 on DeepSearch QA, ahead of similarly sized open models like Gemma4 31B and Qwen3.6 27B on Meta's reporting. These figures are vendor-reported and not independently verified. Where Muse Spark targets large-scale multi-agent orchestration, Muse Glimmer sits a size tier down, aimed at tool use, multi-step reasoning, coding, and LLM-as-a-judge evaluation with a 131,072-token context window. It fits developers who want agentic and coding capability at lower cost than the larger Muse Spark models.
ChatMuse Spark 1.2
Muse Spark 1.2 is Meta Superintelligence Labs' coding-focused update to Muse Spark 1.1, released alongside Muse Code, a terminal coding agent it powers. Meta scaled up training compute on coding tasks and training-environment diversity, aiming at code generation, debugging, codebase understanding, and long-horizon work like whole-repository generation. On Meta's own evaluation harness, Muse Spark 1.2 scored 82.9% on Terminal-Bench 2.1 and 59.3% on DeepSWE 1.1, edging OpenAI's GPT-5.6 Terra (81.8%) and xAI's Grok 4.5 (81.6%) on Terminal-Bench but trailing Anthropic's Opus 5 (86.7%). These are vendor-run numbers, not yet independently verified on the public leaderboards. It keeps the 1,048,576-token context window and text, image, video, audio, and PDF input from 1.1, at the same $1.25 per million input and $4.25 per million output token pricing. A separate contributor tier offers lower rates in exchange for letting Meta train on your prompts and completions.
Frequently Asked Questions
You can access Muse Spark 1.3 by Meta Llama through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Muse Spark 1.3 is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
Muse Spark 1.3 is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $1.25 |
| Output | $4.25 |
Muse Spark 1.3 was created by Meta Llama and released on Sep 2, 2026.
Muse Spark 1.3 supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Muse Spark 1.3 can generate up to 944K tokens in a single response.
Muse Spark 1.3 accepts the following input types: text, image, video, file. It produces: text.
Yes, Muse Spark 1.3 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Muse Spark 1.3 scores 48.1 on the Artificial Analysis Intelligence Index, outperforming 96% of tracked models. On coding, it scores 75.8 (outperforms 90% of models).
Yes — the Muse Spark 1.3 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add Muse Spark 1.3 to your app for free
Developers can integrate Muse Spark 1.3 for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.