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DeepSeek VL2 vs Muse Spark 1.3

Muse Spark 1.3 leads the LLM Stats Score 55.4 to 3.2.

DeepSeek · Meta · Updated for 2026

Which is better?

Muse Spark 1.3 leads the overall LLM Stats Score 55.4 to 3.2, ranking #5 overall.

Muse Spark 1.3 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 DeepSeek VL2

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

Choose Muse Spark 1.3

  • overall performance matters — it scores 55.4 and ranks #5 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

DeepSeek VL2
DeepSeek
Muse Spark 1.3
Meta
Core performance indexes
LLM Stats Score
3.2
#298
55.4
#5
Reasoning
-1.5
#315
53.0
#6
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.10 / M
Output price
— / M
$0.20 / M
Context window
129,280
1,048,576

Individual benchmarks

14 reported for DeepSeek VL2 · 11 for Muse Spark 1.3

No common benchmarks found

DeepSeek VL2 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

Muse Spark 1.3 accepts 1,048,576 input tokens compared to DeepSeek VL2's 129,280 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while DeepSeek VL2 is limited to 129,280 tokens.

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Meta
Muse Spark 1.3
Input1,048,576 tokens
Output943,718 tokens
Thu Sep 03 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek VL2 and Muse Spark 1.3 support multimodal inputs.

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

DeepSeek VL2

Text
Images
Audio
Video

Muse Spark 1.3

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, while Muse Spark 1.3 uses a proprietary license.

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

DeepSeek VL2

deepseek

Open weights

Muse Spark 1.3

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while Muse Spark 1.3 was released on 2026-09-02.

Muse Spark 1.3 is 21 months newer than DeepSeek VL2.

DeepSeek VL2

Dec 13, 2024

1.7 years ago

Muse Spark 1.3

Sep 2, 2026

0 days ago

1.7yr 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

DeepSeek VL2 is available from Replicate. Muse Spark 1.3 is available from Meta Model API.

DeepSeek VL2

replicate logo
Replicate

Muse Spark 1.3

meta logo
Meta
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
* 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 DeepSeek VL2 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.

DeepSeek VL2
✓ Preferred
Muse Spark 1.3
Open in Playground

FAQ

Common questions about DeepSeek VL2 vs Muse Spark 1.3.

Which is better, DeepSeek VL2 or Muse Spark 1.3?

Muse Spark 1.3 leads the LLM Stats Score 55.4 to 3.2. DeepSeek VL2 is made by DeepSeek 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 DeepSeek VL2 compare to Muse Spark 1.3 in benchmarks?

DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. 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 DeepSeek VL2 and Muse Spark 1.3?

DeepSeek VL2 supports 129K 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 DeepSeek VL2 and Muse Spark 1.3?

Key differences include LLM Stats Score (3.2 vs 55.4), context window (129K vs 1.0M), licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 and Muse Spark 1.3?

DeepSeek VL2 is developed by DeepSeek and Muse Spark 1.3 is developed by Meta.

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