Muse Spark 1.3vsGLM-4.6
Muse Spark 1.3 | GLM-4.6 | |
|---|---|---|
| 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. | — | 355B |
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 | 200k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.60 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $2.20 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.11 |
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. | — | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.75Venice |
| BenchmarksPublished by one model only | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 68% |
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 | Z.ai |
| Release date | Sep 2 2026 | Sep 30 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Muse Spark 1.3 and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GLM-4.6 has a verified first-party API price: $0.60 per million input tokens and $2.20 per million output tokens. No pay-as-you-go API rate is tracked for Muse Spark 1.3. GLM-4.6 shipped 337 days before Muse Spark 1.3, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Spark 1.3) vs 200k (GLM-4.6). Muse Spark 1.3 is proprietary, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — Muse Spark 1.3 and GLM-4.6 don't publish scores on any of the same benchmarks.
Muse Spark 1.3 was released by Meta on Sep 2 2026.
GLM-4.6 was released by Z.ai on Sep 30 2025.
Only GLM-4.6 has a verified first-party API price: $0.60 per million input tokens and $2.20 per million output tokens. No pay-as-you-go API rate is tracked for Muse Spark 1.3. Rates are pay-as-you-go API prices verified on August 18, 2026.
Muse Spark 1.3 has a 1M context window; GLM-4.6 has 200k.
Muse Spark 1.3 is a proprietary model released by Meta. GLM-4.6 is an open weight model released by Z.ai.