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ERNIE 5.0 vs Muse Spark 1.3

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 33.6.

Baidu · Meta · Updated for 2026

Which is better?

Muse Spark 1.3 leads the overall LLM Stats Score 55.3 to 33.6, ranking #5 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose ERNIE 5.0

  • you are already invested in the Baidu ecosystem

Choose Muse Spark 1.3

  • overall performance matters — it scores 55.3 and ranks #5 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

ERNIE 5.0
Baidu
Muse Spark 1.3
Meta
Core performance indexes
LLM Stats Score
33.6
#92
55.3
#5
Reasoning
33.1
#90
52.8
#7
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.10 / M
Output price
— / M
$0.20 / M
Context window
—
1,048,576

Individual benchmarks

5 reported for ERNIE 5.0 · 11 for Muse Spark 1.3

No common benchmarks found

ERNIE 5.0 and Muse Spark 1.3don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Muse Spark 1.3 specifies input context (1,048,576 tokens). Only Muse Spark 1.3 specifies output context (943,718 tokens).

Baidu
ERNIE 5.0
Input- tokens
Output- tokens
Meta
Muse Spark 1.3
Input1,048,576 tokens
Output943,718 tokens
Sat Sep 05 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both ERNIE 5.0 and Muse Spark 1.3 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

ERNIE 5.0

Text
Images
Audio
Video

Muse Spark 1.3

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

ERNIE 5.0

Proprietary

Closed source

Muse Spark 1.3

Proprietary

Closed source

Release Timeline

When each model was launched

ERNIE 5.0 was released on 2026-01-22, while Muse Spark 1.3 was released on 2026-09-02.

Muse Spark 1.3 is 7 months newer than ERNIE 5.0.

ERNIE 5.0

Jan 22, 2026

7 months ago

Muse Spark 1.3

Sep 2, 2026

2 days ago

7mo 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→

Judge for yourself.

Run your own prompts against ERNIE 5.0 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.

ERNIE 5.0
✓ Preferred
Muse Spark 1.3
Open in Playground

FAQ

Common questions about ERNIE 5.0 vs Muse Spark 1.3.

Which is better, ERNIE 5.0 or Muse Spark 1.3?

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 33.6. ERNIE 5.0 is made by Baidu and Muse Spark 1.3 is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does ERNIE 5.0 compare to Muse Spark 1.3 in benchmarks?

ERNIE 5.0 scores AIME 2025: 87.0%, MMLU-Pro: 87.0%, GPQA: 85.0%, SimpleQA: 75.0%, Humanity's Last Exam: 39.0%. Muse Spark 1.3 scores MRCR v2 (8-needle): 98.5%, MRCR v2 (8-needle, 512K-1M): 98.1%, DeepSearchQA: 89.4%, Terminal-Bench 2.1: 88.8%, DeepSWE 1.1: 75.4%.

What are the context window sizes for ERNIE 5.0 and Muse Spark 1.3?

ERNIE 5.0 supports an unknown number of tokens and Muse Spark 1.3 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between ERNIE 5.0 and Muse Spark 1.3?

Key differences include LLM Stats Score (33.6 vs 55.3). See the full comparison above for benchmark-by-benchmark results.

Who makes ERNIE 5.0 and Muse Spark 1.3?

ERNIE 5.0 is developed by Baidu and Muse Spark 1.3 is developed by Meta.

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