Model Comparison

GLM-5 vs Muse Spark

Both models are evenly matched across the benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while Muse Spark is better at 1 benchmark (Terminal-Bench 2.0).

Both models are evenly matched across the benchmarks.

Thu Apr 09 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 09 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
Meta
Muse Spark
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Meta
Muse Spark
Input- tokens
Output- tokens
Thu Apr 09 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Muse Spark supports multimodal inputs, whereas GLM-5 does not.

Muse Spark can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

Muse Spark

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Muse Spark uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5

MIT

Open weights

Muse Spark

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Muse Spark was released on 2026-04-08.

Muse Spark is 2 months newer than GLM-5.

GLM-5

Feb 11, 2026

1 months ago

Muse Spark

Apr 8, 2026

1 days ago

1mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
Has open weights
Higher SWE-Bench Verified score (77.8% vs 77.4%)
Meta
Supports multimodal inputs
Higher Terminal-Bench 2.0 score (59.0% vs 56.2%)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Meta
Muse Spark

FAQ

Common questions about GLM-5 vs Muse Spark

Both models are evenly matched across the benchmarks. GLM-5 is made by Zhipu AI and Muse Spark is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Muse Spark scores Tau2 Telecom: 91.5%, GPQA: 89.5%, CharXiv-R: 86.4%, ScreenSpot Pro: 84.1%, IPhO 2025: 82.6%.
GLM-5 supports 200K tokens and Muse Spark supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and Muse Spark is developed by Meta.