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Muse Spark 1.1 vs Parse

Comparing Muse Spark 1.1 and Parse across benchmarks, pricing, and capabilities.

Meta · Cohere · Updated for 2026

Which is better?

Muse Spark 1.1 and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Muse Spark 1.1 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose Muse Spark 1.1

  • you process long inputs — it offers a 1,048,576 token context window

Choose Parse

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
Input price
$1.25 / M
— / M
Output price
$4.25 / M
— / M
Context window
1,048,576
8,192

Individual benchmarks

11 reported for Muse Spark 1.1 · 1 for Parse

No common benchmarks found

Muse Spark 1.1 and Parsedon'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

Muse Spark 1.1 accepts 1,048,576 input tokens compared to Parse's 8,192 tokens. Only Muse Spark 1.1 specifies output context (131,072 tokens).

Meta
Muse Spark 1.1
Input1,048,576 tokens
Output131,072 tokens
Cohere
Parse
Input8,192 tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Muse Spark 1.1 and Parse support multimodal inputs.

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

Muse Spark 1.1

Text
Images
Audio
Video

Parse

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.

Muse Spark 1.1

Proprietary

Closed source

Parse

Proprietary

Closed source

Release Timeline

When each model was launched

Muse Spark 1.1 was released on 2026-07-09, while Parse was released on 2026-08-27.

Parse is 2 months newer than Muse Spark 1.1.

Muse Spark 1.1

Jul 9, 2026

1 months ago

Parse

Aug 27, 2026

3 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

Provider Availability

Muse Spark 1.1 is available from Meta Model API. Parse is available from Azure, Cohere.

Muse Spark 1.1

meta logo
Meta
Input Price:Input: $1.25/1MOutput Price:Output: $4.25/1M

Parse

azure logo
Azure
cohere logo
Cohere
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Muse Spark 1.1 and Parse side-by-side, then vote on the output you prefer.

Muse Spark 1.1
✓ Preferred
Parse
Open in Playground

FAQ

Common questions about Muse Spark 1.1 vs Parse.

Which is better, Muse Spark 1.1 or Parse?

Muse Spark 1.1 (Meta) and Parse (Cohere) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Muse Spark 1.1 compare to Parse in benchmarks?

Muse Spark 1.1 scores CharXiv-R: 88.4%, MCP Atlas: 88.1%, OSWorld-Verified: 80.8%, Terminal-Bench 2.1: 80.0%, BabyVision: 76.3%. Parse scores ParseBench: 79.2%.

What are the context window sizes for Muse Spark 1.1 and Parse?

Muse Spark 1.1 supports 1.0M tokens and Parse supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Muse Spark 1.1 and Parse?

Key differences include context window (1.0M vs 8K). See the full comparison above for benchmark-by-benchmark results.

Who makes Muse Spark 1.1 and Parse?

Muse Spark 1.1 is developed by Meta and Parse is developed by Cohere.