Model Comparison

DeepSeek-V2.5 vs Muse Spark 1.1Which is better in 2026?

Comparing DeepSeek-V2.5 and Muse Spark 1.1 across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V2.5 vs Muse Spark 1.1 — which is better?

DeepSeek-V2.5 (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.

On price, DeepSeek-V2.5 is roughly 11.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

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.

Choose DeepSeek-V2.5 if…

  • cost matters — it's about 11.4x cheaper per token
  • you need open weights you can self-host or fine-tune

Choose Muse Spark 1.1 if…

  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Jul 2026

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V2.5 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

Pricing Analysis

Price comparison per million tokens

DeepSeek-V2.5 costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 8.9x cheaper than Muse Spark 1.1 ($1.25/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 15.2x cheaper than Muse Spark 1.1 ($4.25/1M tokens).

In conclusion, Muse Spark 1.1 is more expensive than DeepSeek-V2.5.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sat Jul 18 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Muse Spark 1.1
Input tokens$1.25
Output tokens$4.25
Best providerMeta
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Muse Spark 1.1 accepts 1,048,576 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Muse Spark 1.1 can generate longer responses up to 131,072 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Meta
Muse Spark 1.1
Input1,048,576 tokens
Output131,072 tokens
Sat Jul 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Muse Spark 1.1 supports multimodal inputs, whereas DeepSeek-V2.5 does not.

Muse Spark 1.1 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V2.5

Text
Images
Audio
Video

Muse Spark 1.1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V2.5 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-V2.5

deepseek

Open weights

Muse Spark 1.1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Muse Spark 1.1 was released on 2026-07-09.

Muse Spark 1.1 is 26 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.2 years ago

Muse Spark 1.1

Jul 9, 2026

1 weeks ago

2.2yr 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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Muse Spark 1.1 is available from Meta Model API.

DeepSeek-V2.5

deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M

Muse Spark 1.1

meta logo
Meta
Input Price:Input: $1.25/1MOutput Price:Output: $4.25/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Less expensive input tokens
Less expensive output tokens
Has open weights
Meta
Larger context window (1,048,576 tokens)
Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V2.5 and Muse Spark 1.1 side-by-side, then vote on the output you prefer.

DeepSeek-V2.5
✓ Preferred
Muse Spark 1.1
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Muse Spark 1.1

FAQ

Common questions about DeepSeek-V2.5 vs Muse Spark 1.1.

Which is better, DeepSeek-V2.5 or Muse Spark 1.1?

DeepSeek-V2.5 (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-V2.5 compare to Muse Spark 1.1 in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. 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%.

Is DeepSeek-V2.5 cheaper than Muse Spark 1.1?

DeepSeek-V2.5 is 8.9x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. Muse Spark 1.1 costs $1.25/M input and $4.25/M output via meta.

What are the context window sizes for DeepSeek-V2.5 and Muse Spark 1.1?

DeepSeek-V2.5 supports 8K 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-V2.5 and Muse Spark 1.1?

Key differences include context window (8K vs 1.0M), input pricing ($0.14 vs $1.25/M), multimodal support (no vs yes), licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Muse Spark 1.1?

DeepSeek-V2.5 is developed by DeepSeek and Muse Spark 1.1 is developed by Meta.

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