Muse Spark 1.3vsQwen1.5
Muse Spark 1.3 | Qwen1.5 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 72B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | 32k |
| BenchmarksPublished by one model only | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 75.4% | — |
SWEAtlas CodeBase QnACodebase understanding — Questions about how an unfamiliar codebase actually works — where something is handled, what a change would touch — answered by reading the repository rather than editing it. Tests understanding rather than patch-writing. Higher is better. | 59.4% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 88.8% | — |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | 64.9% | — |
DeepSearchQAAgentic browsing — Questions that cannot be answered from one page: the AI has to search the web, follow the trail across several sources, and put the pieces together into an answer. Higher is better. | 89.4% | — |
Agentic IF Index (Internal)Instruction following — Meta's internal measure of whether a model keeps following the instructions it was given while working as an agent — over a long run of tool calls, not just in a single reply. Higher is better. | 57.8% | — |
OSWorld 2.0Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | 66.9% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 49.4% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1754 | — |
MRCR · 256k-512kLong context — Tests whether the AI can find specific details buried inside a very long document, here across inputs of roughly 256k to 512k tokens — several books' worth of text. Higher is better. | 98.5% | — |
MRCR · 512k-1MLong context — The same buried-detail retrieval test run on even longer inputs, from roughly 512k up to a million tokens. Scores usually slip as the document grows, so read it against the shorter span above. Higher is better. | 98.1% | — |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Sep 2 2026 | Feb 4 2024 |
| Access | Proprietary | Open Weight |
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Frequently asked questions
Muse Spark 1.3 and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen1.5 shipped 941 days before Muse Spark 1.3, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Spark 1.3) vs 32k (Qwen1.5). Muse Spark 1.3 is proprietary, while Qwen1.5 is open weight.
Direct benchmark comparisons are unavailable — Muse Spark 1.3 and Qwen1.5 don't publish scores on any of the same benchmarks.
Muse Spark 1.3 was released by Meta on Sep 2 2026.
Qwen1.5 was released by Qwen on Feb 4 2024.
Muse Spark 1.3 has a 1M context window; Qwen1.5 has 32k.
Muse Spark 1.3 is a proprietary model released by Meta. Qwen1.5 is an open weight model released by Qwen.