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DeepSeek-V4-Pro-Max vs Muse Spark 1.3

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 43.5. Muse Spark 1.3 is 16.0x cheaper per token.

DeepSeek · Meta · Updated for 2026

Which is better?

Muse Spark 1.3 leads the overall LLM Stats Score 55.3 to 43.5, ranking #5 overall.

On price, Muse Spark 1.3 is roughly 16.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

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

Choose DeepSeek-V4-Pro-Max

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

Choose Muse Spark 1.3

  • overall performance matters — it scores 55.3 and ranks #5 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 16.0x cheaper per token
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

DeepSeek-V4-Pro-Max
DeepSeek
Muse Spark 1.3
Meta
Core performance indexes
LLM Stats Score
43.5
#37
55.3
#5
Reasoning
43.7
#37
52.8
#7
Coding
32.2
#36
41.7
#10
Agents
23.7
#51
40.4
#4
Cost, coverage & limits
Benchmark wins
—
—
Input price
$1.60 / M
$0.10 / M
Output price
$3.20 / M
$0.20 / M
Context window
1,048,576
1,048,576

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V4-Pro-Max
Muse Spark 1.3
Tool use
22.8#41
35.5#2
Long context
17.8#37
31.7#1
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

22 reported for DeepSeek-V4-Pro-Max · 11 for Muse Spark 1.3

No common benchmarks found

DeepSeek-V4-Pro-Max 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

Pricing Analysis

Price comparison per million tokens

Muse Spark 1.3 costs less

For input processing, DeepSeek-V4-Pro-Max ($1.60/1M tokens) is 16.0x more expensive than Muse Spark 1.3 ($0.10/1M tokens).

For output processing, DeepSeek-V4-Pro-Max ($3.20/1M tokens) is 16.0x more expensive than Muse Spark 1.3 ($0.20/1M tokens).

In conclusion, DeepSeek-V4-Pro-Max is more expensive than Muse Spark 1.3.*

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

Lowest available price from all providers
Sat Sep 05 2026 • llm-stats.com
DeepSeek
DeepSeek-V4-Pro-Max
Input tokens$1.60
Output tokens$3.20
Best providerNovita
Meta
Muse Spark 1.3
Input tokens$0.10
Output tokens$0.20
Best providerMeta
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Both models have the same input context window of 1,048,576 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while DeepSeek-V4-Pro-Max is limited to 131,072 tokens.

DeepSeek
DeepSeek-V4-Pro-Max
Input1,048,576 tokens
Output131,072 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

Muse Spark 1.3 supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.

Muse Spark 1.3 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Pro-Max

Text
Images
Audio
Video

Muse Spark 1.3

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Pro-Max is licensed under MIT, 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-V4-Pro-Max

MIT

Open weights

Muse Spark 1.3

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Pro-Max was released on 2026-04-23, while Muse Spark 1.3 was released on 2026-09-02.

Muse Spark 1.3 is 4 months newer than DeepSeek-V4-Pro-Max.

DeepSeek-V4-Pro-Max

Apr 23, 2026

4 months ago

Muse Spark 1.3

Sep 2, 2026

3 days ago

4mo 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-V4-Pro-Max is available from Novita, DeepInfra, DeepSeek, Fireworks, Together. Muse Spark 1.3 is available from Meta Model API.

DeepSeek-V4-Pro-Max

novita logo
Novita
Input Price:Input: $1.60/1MOutput Price:Output: $3.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $1.74/1MOutput Price:Output: $3.48/1M
deepseek logo
DeepSeek
Input Price:Input: $1.74/1MOutput Price:Output: $3.48/1M
fireworks logo
Fireworks
Input Price:Input: $1.74/1MOutput Price:Output: $3.48/1M
together logo
Together
Input Price:Input: $1.74/1MOutput Price:Output: $3.48/1M

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-V4-Pro-Max and Muse Spark 1.3 side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-Max
✓ Preferred
Muse Spark 1.3
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-Max vs Muse Spark 1.3.

Which is better, DeepSeek-V4-Pro-Max or Muse Spark 1.3?

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 43.5. DeepSeek-V4-Pro-Max 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-V4-Pro-Max compare to Muse Spark 1.3 in benchmarks?

DeepSeek-V4-Pro-Max scores CodeForces: 100.0%, HMMT Feb 26: 95.2%, LiveCodeBench: 93.5%, MathArena Apex: 90.2%, GPQA: 90.1%. 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%.

Is DeepSeek-V4-Pro-Max cheaper than Muse Spark 1.3?

Muse Spark 1.3 is 16.0x cheaper for input tokens. DeepSeek-V4-Pro-Max costs $1.60/M input and $3.20/M output via novita. Muse Spark 1.3 costs $0.10/M input and $0.20/M output via meta.

What are the context window sizes for DeepSeek-V4-Pro-Max and Muse Spark 1.3?

DeepSeek-V4-Pro-Max supports 1.0M 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-V4-Pro-Max and Muse Spark 1.3?

Key differences include LLM Stats Score (43.5 vs 55.3), input pricing ($1.60 vs $0.10/M), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Pro-Max and Muse Spark 1.3?

DeepSeek-V4-Pro-Max is developed by DeepSeek and Muse Spark 1.3 is developed by Meta.

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