AI model comparison

Muse Spark Open vs GLM 5.3

Side by side comparison of Muse Spark Open and GLM 5.3 on pricing, benchmark scores, context window, and more.

Muse Spark Open is a coding-optimized open-weight model.

Input / 1M
$1.25
Output / 1M
$4.25
Context
1.05M

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks.

Input / 1M
$0.08
Output / 1M
$3.60
Context
1.05M

Comparing image generators? Compare image models

At a glance

Quick verdict

How the two models stack up on the things people ask about most.

Lower price

z-ai logo

GLM 5.3

2.4x cheaper for a typical chat

Higher intelligence score

z-ai logo

GLM 5.3

31.7 vs 24.7 on Artificial Analysis

Larger context window

Tie

1.05M vs 1.05M tokens

Newer release

meta logo

Muse Spark Open

Released October 15, 2026

Benchmarks

Benchmark scores

Independent scores from Artificial Analysis. Higher is better.

Intelligence index

meta logoMuse Spark Open24.7
z-ai logoGLM 5.331.7

Coding index

meta logoMuse Spark OpenNot available
z-ai logoGLM 5.374.8

Agentic index

meta logoMuse Spark OpenNot available
z-ai logoGLM 5.353.1

Pricing

Muse Spark Open vs GLM 5.3 API pricing

Per-token API rates. Cheaper option highlighted.

Metricmeta logoMuse Spark Openz-ai logoGLM 5.3
Input tokensPer 1M tokens$1.25$0.08
Output tokensPer 1M tokens$4.25$3.60
Cached input (read)Per 1M tokens$0.15$0.08
Cache writePer 1M tokensNot availableNot available

What it costs in practice

Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.

Chat message

2K in, 500 out

meta logoMuse Spark Open$4.63
z-ai logoGLM 5.3$1.96

Coding task

30K in, 4K out

meta logoMuse Spark Open$54.50$21.50 cached
z-ai logoGLM 5.3$16.80$16.68 cached

Long document summary

150K in, 2K out

meta logoMuse Spark Open$196$31 cached
z-ai logoGLM 5.3$19.20$18.60 cached

Specs

Context window and capabilities

How much each model can read, how much it can write, and what it accepts as input.

Metricmeta logoMuse Spark Openz-ai logoGLM 5.3
Context window1.05M tokens1.05M tokens
Max output944K tokens944K tokens
Input typesAudio, File, Image, Text, VideoText
Output typesTextText
Reasoning effort levelsExtra high, High, Medium, LowMax, High, Low
Default reasoning effortMediumMax
Release dateOctober 15, 2026August 18, 2026

Features

Supported features

API features available for each model.

Metricmeta logoMuse Spark Openz-ai logoGLM 5.3
Tool callingSupportedSupported
Structured outputsSupportedSupported
JSON modeSupportedSupported
ReasoningSupportedSupported
TemperatureSupportedSupported
Stop sequencesSupportedSupported
Deterministic seedSupportedSupported
Verbosity controlNot supportedNot supported

Our take

GLM 5.3 is 2.4x cheaper for a typical chat. GLM 5.3 scores higher on the Artificial Analysis Intelligence Index (31.7 vs 24.7). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.

FAQ

Frequently asked questions

Is Muse Spark Open or GLM 5.3 cheaper?

GLM 5.3 is cheaper for a typical chat (2K input tokens and 500 output tokens). GLM 5.3 costs $0.08 per 1M input tokens and $3.60 per 1M output tokens, while Muse Spark Open costs $1.25 per 1M input tokens and $4.25 per 1M output tokens.

Which has a bigger context window, Muse Spark Open or GLM 5.3?

Muse Spark Open supports up to 1.05M tokens of context, compared with 1.05M for GLM 5.3.

Is Muse Spark Open smarter than GLM 5.3?

On the Artificial Analysis Intelligence Index, Muse Spark Open scores 24.7 and GLM 5.3 scores 31.7, putting GLM 5.3 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.

Can I use Muse Spark Open and GLM 5.3 at the same time?

Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Muse Spark Open and GLM 5.3 answer side by side and keep the better response.

meta logoz-ai logo

Why choose? Ask both.

Send one prompt to Muse Spark Open and GLM 5.3 at the same time. Compare the answers side by side and keep the best one.