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Discover how Meta's Muse Spark 1.3 and DeepSeek's DeepSeek V4 Pro stack up against each other in this comprehensive comparison of two leading AI language models.

Explore their capabilities, pricing, and performance metrics to find the right AI solution for your specific needs.

Models Overview

	Muse Spark 1.3	DeepSeek V4 Pro
Provider The company that provides the model.	Meta	DeepSeek
Context Length Maximum number of tokens the model can process	1.05M	1M
Maximum Output Maximum number of tokens the model can generate in one response	Unknown	384K
Release Date When the model was first released.	02-09-2026	Unknown
Knowledge Cutoff When the model's training data ends.	Unknown	Unknown
Open Source Whether the model weights are openly available.	FALSE	TRUE

Pricing Comparison

Compare the pricing of Meta's Muse Spark 1.3 and DeepSeek's DeepSeek V4 Pro to determine the most cost-effective solution for your AI needs. Prices are the standard API tier per million tokens, as published by each provider as of September 2026.

	Muse Spark 1.3	DeepSeek V4 Pro
Input Cost Cost per million input tokens	Pricing not available	$1.32 / 1M tokens
Output Cost Cost per million tokens generated	Pricing not available	$3.96 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Meta's Muse Spark 1.3 and DeepSeek's DeepSeek V4 Pro on industry benchmarks. Scores are the ones the providers and public leaderboards report; a benchmark neither reports is left out.

	Muse Spark 1.3	DeepSeek V4 Pro
LMArena Elo Crowd-sourced blind preference rating on the LMArena text leaderboard.	1,493	Benchmark not available
GPQA Diamond Graduate-level science questions written to be search-proof.	Benchmark not available	90.1%
SWE-bench Verified Resolving real GitHub issues end to end.	Benchmark not available	80.6%
MMLU-Pro Broad knowledge and reasoning across 14 subjects, harder successor of MMLU.	Benchmark not available	87.5%
HumanEval Functional correctness of generated code.	Benchmark not available	76.8%