AI model comparison

GLM 5.3 vs Muse Spark 1.2

Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.

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

Input / 1M
$0.04
Output / 1M
$7
Context
1.05M

Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks.

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

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

1.3x cheaper for a typical chat

Higher intelligence score

z-ai logo

GLM 5.3

44.8 vs 39.6 on Artificial Analysis

Larger context window

Tie

1.05M vs 1.05M tokens

Newer release

z-ai logo

GLM 5.3

Released August 18, 2026

Benchmarks

Benchmark scores

Independent scores from Artificial Analysis. Higher is better.

Intelligence index

z-ai logoGLM 5.344.8
meta logoMuse Spark 1.239.6

Coding index

z-ai logoGLM 5.374.8
meta logoMuse Spark 1.272.2

Agentic index

z-ai logoGLM 5.353.1
meta logoMuse Spark 1.243.2

Pricing

GLM 5.3 vs Muse Spark 1.2 API pricing

Per-token API rates. Cheaper option highlighted.

Metricz-ai logoGLM 5.3meta logoMuse Spark 1.2
Input tokensPer 1M tokens$0.04$1.25
Output tokensPer 1M tokens$7$4.25
Cached input (read)Per 1M tokens$0.04$0.15
Cache writePer 1M tokensNot availableNot available
Web searchPer requestNot available$0.0025

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

z-ai logoGLM 5.3$3.58
meta logoMuse Spark 1.2$4.63

Coding task

30K in, 4K out

z-ai logoGLM 5.3$29.20$29.17 cached
meta logoMuse Spark 1.2$54.50$21.50 cached

Long document summary

150K in, 2K out

z-ai logoGLM 5.3$20$19.85 cached
meta logoMuse Spark 1.2$196$31 cached

Specs

Context window and capabilities

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

Metricz-ai logoGLM 5.3meta logoMuse Spark 1.2
Context window1.05M tokens1.05M tokens
Max output944K tokens944K tokens
Input typesTextFile, Image, Text, Video
Output typesTextText
Reasoning effort levelsMax, High, LowExtra high, High, Medium, Low, Minimal
Default reasoning effortMaxMedium
Release dateAugust 18, 2026August 5, 2026

Features

Supported features

API features available for each model.

Metricz-ai logoGLM 5.3meta logoMuse Spark 1.2
Tool callingSupportedSupported
Structured outputsSupportedSupported
JSON modeSupportedSupported
ReasoningSupportedSupported
TemperatureSupportedSupported
Stop sequencesSupportedNot supported
Deterministic seedSupportedNot supported
Verbosity controlNot supportedNot supported

Our take

GLM 5.3 is 1.3x cheaper for a typical chat. GLM 5.3 scores higher on the Artificial Analysis Intelligence Index (44.8 vs 39.6). 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 GLM 5.3 or Muse Spark 1.2 cheaper?

GLM 5.3 is cheaper for a typical chat (2K input tokens and 500 output tokens). GLM 5.3 costs $0.04 per 1M input tokens and $7 per 1M output tokens, while Muse Spark 1.2 costs $1.25 per 1M input tokens and $4.25 per 1M output tokens.

Which has a bigger context window, GLM 5.3 or Muse Spark 1.2?

GLM 5.3 supports up to 1.05M tokens of context, compared with 1.05M for Muse Spark 1.2.

Is GLM 5.3 smarter than Muse Spark 1.2?

On the Artificial Analysis Intelligence Index, GLM 5.3 scores 44.8 and Muse Spark 1.2 scores 39.6, putting GLM 5.3 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.

Can I use GLM 5.3 and Muse Spark 1.2 at the same time?

Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 5.3 and Muse Spark 1.2 answer side by side and keep the better response.

z-ai logometa logo

Why choose? Ask both.

Send one prompt to GLM 5.3 and Muse Spark 1.2 at the same time. Compare the answers side by side and keep the best one.