Muse Spark 1.2vsGLM-5.3-Flash
Muse Spark 1.2 | GLM-5.3-Flash | |
|---|---|---|
| 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. | — | 320B |
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 | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.25Meta | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $4.25Meta | — |
| Benchmarks | ||
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. | 59.3% | 63.4% |
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. | 82.9% | 84.3% |
| BenchmarksPublished by one model only | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 50% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 55.3% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 26.3% |
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. | — | 48.8% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1773 |
| Overview | ||
| Company | Meta | Z.ai |
| Release date | Aug 5 2026 | Aug 26 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
GLM-5.3-Flash leads Muse Spark 1.2 on 2 of the 2 benchmarks they both report (DeepSWE 1.1, Terminal-Bench 2.1). Muse Spark 1.2 shipped 21 days before GLM-5.3-Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Spark 1.2) vs 1M (GLM-5.3-Flash). Muse Spark 1.2 is proprietary, while GLM-5.3-Flash is open weight.
On DeepSWE 1.1, GLM-5.3-Flash leads at 63.4% vs Muse Spark 1.2 at 59.3%. On Terminal-Bench 2.1, GLM-5.3-Flash leads at 84.3% vs Muse Spark 1.2 at 82.9%.
Muse Spark 1.2 was released by Meta on Aug 5 2026.
GLM-5.3-Flash was released by Z.ai on Aug 26 2026.
GLM-5.3-Flash leads on DeepSWE 1.1 — Muse Spark 1.2 59.3% vs GLM-5.3-Flash 63.4%.
Muse Spark 1.2 has a 1M context window; GLM-5.3-Flash has 1M.
Muse Spark 1.2 is a proprietary model released by Meta. GLM-5.3-Flash is an open weight model released by Z.ai.