Muse Spark 1.1 vs DeepSeek-V4 Pro
Head-to-head technical comparison of intelligence, capabilities, and API pricing.
Which should you use?
Muse Spark 1.1 and DeepSeek-V4 Pro score within 0.5 points of each other on the composite index, which is too close to call a winner on capability alone.
Cost is the sharper difference: Muse Spark 1.1 is 2.9x the input price of DeepSeek-V4 Pro ($1.25 against $0.435 per 1M), so at volume the choice is usually decided by budget rather than benchmarks.
Pick Muse Spark 1.1 when
- Ahead on Arena Elo by 43 points (1492 vs 1449), which is outside the board's published confidence intervals.
- Stronger on MMLU-Pro: 91.9% against 91.8%.
Pick DeepSeek-V4 Pro when
- Cheaper input tokens — $0.435 against $1.25 per 1M, so Muse Spark 1.1 costs 2.9x as much to feed.
- Better value per point of measured capability: $0.005 per index point against $0.014.
Value per point of measured capability, at list input pricing: DeepSeek-V4 Pro $0.005 · Muse Spark 1.1 $0.014 per index point. Run your own token mix through the token cost calculator — a comparison at list price ignores caching and batch discounts, which move real bills more than this gap does.
Where these numbers come from
Figures as of 2026-09-14. Arena Elo and pricing are read from the public board and each vendor's own pricing page; see the full leaderboard for all 34 models and which columns are measured rather than estimated, or the benchmark matrix for scores benchmark by benchmark.
Our reporting on Meta and DeepSeek
Meta's Muse Spark Open Puts 816B MoE on Two Mac Studios
Meta's open-weight 816B MoE activates 42B parameters, holds 1M context, runs on two M5 Ultra Mac Studios, and leads on MCP-Atlas but trails on coding.
Meta's Muse Wonder Debuts at $4/$20—Wonder Full Is API-Gated
Meta launches Muse Wonder at $4 input and $20 output per million tokens, but Muse Wonder Full stays locked to Muse Code subscribers.
We Planted 20 Wrong Figures for 4 LLM Editors. Here's What Survived
We replayed real edit prompts against Sonnet 4.6, DeepSeek V4 Pro, GPT-5.6 Sol and Gemini 3.1 Pro. Self-reported confidence told us nothing.