Model Comparison

DeepSeek VL2 Tiny vs Muse Spark 1.1Which is better in 2026?

Comparing DeepSeek VL2 Tiny and Muse Spark 1.1 across benchmarks, pricing, and capabilities.

Verdict: DeepSeek VL2 Tiny vs Muse Spark 1.1 — which is better?

DeepSeek VL2 Tiny (by DeepSeek) and Muse Spark 1.1 (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose DeepSeek VL2 Tiny if…

  • you need open weights you can self-host or fine-tune

Choose Muse Spark 1.1 if…

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

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek VL2 Tiny and Muse Spark 1.1don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Muse Spark 1.1 specifies input context (1,048,576 tokens). Only Muse Spark 1.1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek VL2 Tiny
Input- tokens
Output- tokens
Meta
Muse Spark 1.1
Input1,048,576 tokens
Output131,072 tokens
Sun Jul 19 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 Tiny and Muse Spark 1.1 support multimodal inputs.

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

DeepSeek VL2 Tiny

Text
Images
Audio
Video

Muse Spark 1.1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Tiny is licensed under deepseek, while Muse Spark 1.1 uses a proprietary license.

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

DeepSeek VL2 Tiny

deepseek

Open weights

Muse Spark 1.1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek VL2 Tiny was released on 2024-12-13, while Muse Spark 1.1 was released on 2026-07-09.

Muse Spark 1.1 is 19 months newer than DeepSeek VL2 Tiny.

DeepSeek VL2 Tiny

Dec 13, 2024

1.6 years ago

Muse Spark 1.1

Jul 9, 2026

1 weeks ago

1.6yr 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

DeepSeek
Has open weights
Meta
Larger context window (1,048,576 tokens)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek VL2 Tiny and Muse Spark 1.1 side-by-side, then vote on the output you prefer.

DeepSeek VL2 Tiny
✓ Preferred
Muse Spark 1.1
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2 Tiny
Meta
Muse Spark 1.1

FAQ

Common questions about DeepSeek VL2 Tiny vs Muse Spark 1.1.

Which is better, DeepSeek VL2 Tiny or Muse Spark 1.1?

DeepSeek VL2 Tiny (DeepSeek) and Muse Spark 1.1 (Meta) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek VL2 Tiny compare to Muse Spark 1.1 in benchmarks?

DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%. 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%.

What are the context window sizes for DeepSeek VL2 Tiny and Muse Spark 1.1?

DeepSeek VL2 Tiny supports an unknown number of tokens and Muse Spark 1.1 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 DeepSeek VL2 Tiny and Muse Spark 1.1?

Key differences include licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 Tiny and Muse Spark 1.1?

DeepSeek VL2 Tiny is developed by DeepSeek and Muse Spark 1.1 is developed by Meta.

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