Muse Spark Open vs DeepSeek V4.1 Flash

Quick Answer

These two represent different bets on agent economics. Muse Spark Open is a large multimodal model, 816B parameters with 42B active per token, that accepts audio and video and targets long-horizon agent work. DeepSeek V4.1 Flash is a lean execution model, 552B parameters but only about 8B to 16B active, built to be cheap and fast. On the independent index we track, the lean model currently scores higher, runs faster, and costs about a third as much per task.

For high-volume agent execution, DeepSeek V4.1 Flash is the stronger value. Muse Spark Open makes sense when you need its multimodal input or prefer its provider ecosystem and license.

At a Glance

Two Approaches to Agent Economics

An agent may call a model hundreds of times to finish one task. What that costs depends on price per token, how many tokens each call produces, and how often calls fail.

DeepSeek’s approach is to activate very few parameters per token, which makes each call cheap and fast, and accept that the model writes a lot when it reasons: V4.1 Flash used about twice as many output tokens as Spark Open on Artificial Analysis’s index. Even so, it finished at about a third of the cost per task, and scored higher.

Meta’s approach is a larger active model that writes less per task and brings native multimodal input. The independent index does not reward that on general tasks, but for workflows that need to understand audio or video, it is the only one of the two that can.

See cost per completed task for why per-task cost is the number to compare.

Self-Hosting and Licensing

Both are open-weight. DeepSeek’s MIT license is about as permissive as it gets. Meta’s license requires a “Built with Muse Spark Open” notice and a separate agreement before offering it as an API service with more than $20 million in annual revenue, and downloads require an identity form.

Neither fits on a single consumer GPU. V4.1 Flash’s roughly 510GB FP8 checkpoint is smaller than Spark Open’s 700GB-plus NVFP4 requirement, and its low active-parameter count makes it cheaper to serve once loaded. Meta’s MLX path on two M5 Ultra Mac Studios is a more accessible option for some teams than a GPU cluster.

Data and Deployment

Self-hosting either keeps your data on your infrastructure. If you use DeepSeek’s own hosted API, note that DeepSeek is based in China, which matters for some organizations’ data policies; third-party hosts serve both models in other regions. Spark Open is served by US and European providers including Baseten, Fireworks AI, Groq, Together AI, and Nebius.

Choose Muse Spark Open If

You need audio or video understanding, you prefer US-based model provenance and hosting, you want provider choice, or the Muse Model License terms suit your product.

Choose DeepSeek V4.1 Flash If

Cost and speed per task drive your decision, you want an MIT license, you run high-volume agent or coding loops, or you want the smaller self-hosting footprint.

When Neither Is Ideal

For hard planning and judgment steps, both trail closed frontier models; route those to a stronger model and keep the open model for execution, the planner-worker pattern. For an even cheaper worker, see MiniMax M3.1 Flash vs DeepSeek V4.1 Flash.

Final Recommendation

DeepSeek V4.1 Flash is the better default for open-weight agent execution on current independent evidence. Choose Muse Spark Open when its multimodal input or licensing and provenance make it the better fit for your product.

Related Comparisons

• Muse Spark Open vs Mistral Large 4
• MiniMax M3.1 Flash vs DeepSeek V4.1 Flash
• Qwen vs Kimi vs DeepSeek vs GLM