Meta Muse Spark vs DeepSeek V4.1 Flash: Which AI Wins?

Meta's Muse Spark Open and DeepSeek's V4.1 Flash-1017 are the two open-weight models everyone searching "open weight AI model 2026" is actually comparing right now, and the honest answer is that neither one wins cleanly. Third-party benchmark aggregators vals.ai and BenchLM show the two models splitting wins depending on which version you test and which task you care about: DeepSeek V4.1 Flash-1017 tends to lead on raw coding and terminal benchmarks, while Muse Spark Open tends to lead on legal and knowledge-heavy tasks. The bigger practical differences are elsewhere: Meta's model is nearly twice the parameter count but ships under a custom license with commercial strings attached, while DeepSeek's model is the leaner, cheaper, plain-MIT option.

What Meta Muse Spark Open actually is

Muse Spark Open is Meta's open-weight release of its Muse family, published through Meta's developer platform and Hugging Face on October 15, 2026. Meta's own model page describes it as "the most capable open-weight model from a US lab," positioning it directly against Chinese open-weight releases like DeepSeek's, Moonshot's Kimi and Zhipu's GLM. It is derived from the closed Muse Spark model Meta launched in April 2026 under its new Superintelligence Labs division, with what Meta calls additional safety post-training so it can run standalone without the guardrails of the hosted Meta AI product.

The model totals 816 billion parameters with 42 billion active per token, using a mixture-of-experts transformer with a perception encoder, 256 experts, and 8 activated per forward pass, according to its Hugging Face model card. It accepts text, image, video and audio input and produces text output, with a context window of up to 1 million tokens. Meta lists Muse Spark Open's primary use cases as coding agents, multimodal document and video perception, multi-agent and end-to-end agentic workflows, and computer-use tasks such as controlling a browser or desktop environment.

For deployment, Meta lists the weights as available directly from Hugging Face, through Amazon SageMaker JumpStart, Google Cloud Model Garden and Microsoft Foundry, and via inference partners including Baseten, Fireworks AI, Groq, Together AI, OpenRouter, Modal and Nebius. For background on the release itself, see Pandromeda's coverage of the Muse Spark Open launch.

What DeepSeek V4.1 Flash-1017 actually is

DeepSeek V4.1 Flash first shipped on September 10, 2026, as the company's leaner, faster successor to V4-Pro. On October 17, 2026, DeepSeek pushed what it calls a "minor update" to that model, officially named DeepSeek-V4.1-Flash-1017, with claimed gains in coding, agentic task completion and visual understanding, plus shorter outputs and faster task completion. DeepSeek's own API documentation confirms the practical effect for developers: the `deepseek-flash` model alias in the API and app is "now served by DeepSeek-V4.1-Flash-1017," so anyone already building on the deepseek-flash endpoint is on the new build automatically.

Specs are less tidy than Meta's. DeepSeek's Hugging Face model card describes a "552B backbone" built as a 40-layer causal encoder-decoder transformer, with 1 shared expert and 384 routed experts per mixture-of-experts layer and 6 routed experts activated per token, plus a separate 196-billion-parameter "Engram" conditional memory module and a Compressed Sparse Attention 2 mechanism for cheaper long-context serving. The safetensors file metadata for the repository lists a 763B total parameter count, which is the figure most third-party trackers now cite for the full model. Active parameters are listed at 8 billion per token during prefill and 16 billion during decode. Context length reaches 1 million tokens, with a maximum output of 384,000 tokens. The weights are released under the plain MIT license. More on the release is in Pandromeda's report on the V4.1 Flash-1017 update.

Muse Spark Open vs DeepSeek V4.1 Flash-1017: spec for spec

License and commercial terms: the real difference

The specs table hides what may be the most consequential difference between these two releases: the license. DeepSeek ships V4.1 Flash-1017 under the plain MIT license, the same permissive terms used across its V4 family, with essentially no restrictions on commercial use, modification or redistribution.

Muse Spark Open instead uses Meta's Muse Model License, a custom license rather than a standard open-source one. Reporting on the license describes it as allowing free commercial use and fine-tuning for most developers, but requiring companies that offer the model as a hosted service above a revenue threshold to sign a separate agreement with Meta, and requiring products built on it to display "Built with Muse Spark Open" branding. Meta's acceptable-use terms also reportedly bar use in critical infrastructure, military applications and other high-risk categories. That puts Muse Spark Open closer to Meta's existing Llama community license model than to a true open-source license like MIT or Apache 2.0, even though the weights themselves are fully downloadable.

Third-party benchmarks: a split decision on vals.ai

Because neither company's own benchmark numbers are directly comparable, the clearest side-by-side data comes from third-party aggregator vals.ai, which runs both models through the same test suites. The results are genuinely mixed, and vary by which version of each model is being tested:

• DeepSeek V4.1 Flash vs. Muse Spark 1.2: vals.ai's head-to-head shows DeepSeek with the higher score on 12 of 20 shared benchmarks and Muse Spark leading on 7, with overall Vals Index scores close (DeepSeek 51.32% vs. Muse Spark 49.29%). DeepSeek led by wide margins on IOI, SkillsBench and Terminal-Bench 4.0; Muse Spark's biggest edge was on Harvey's Legal Agent Benchmark, roughly 25% to DeepSeek's 6.7%.
• DeepSeek V4.1 Flash-1017 vs. Muse Spark 1.3 Max: on this newer pairing, Muse Spark led on 6 of 9 shared benchmarks, with Vals Index scores of 58.16% for Muse Spark versus 56.58% for DeepSeek. Muse Spark's largest lead was again on legal benchmarks; DeepSeek still led on Terminal-Bench 4.0 and narrowly on coding-heavy Vibe Code Bench and Tax Agent Bench.

Vals.ai is explicit that its benchmark comparisons are not formal statistical significance tests, and several of its scores have overlapping uncertainty ranges between the two models, meaning the "gap" on some rows may not be meaningfully different at all.

Third-party benchmarks: BenchLM's composite scores

Benchmark aggregator BenchLM.ai runs a separate, broader composite across categories like agentic, coding, knowledge and multimodal tasks, and its picture also shifts by version. Comparing DeepSeek V4.1 Flash-1017 against Muse Spark 1.1, BenchLM gives Muse Spark a narrow overall edge (66.06 vs. 65.65), built from DeepSeek leading the Agentic and Coding categories while Muse Spark leads Knowledge; several category rows are marked "not comparable" due to too few shared benchmarks. BenchLM's page for Muse Spark 1.3 shows a wider gap in Muse Spark's favor, 69 to 55 overall, though that page itself flags some rows as still "coming soon" and should be treated as provisional. BenchLM also lists DeepSeek V4.1 Flash-1017 as the cheaper model on published output-token pricing in every comparison it runs against Muse Spark.

Taken together, the aggregator data supports one conclusion more than any other: there is no single, stable winner. Which model "wins" depends on the exact version pair being tested, the task category, and which aggregator's methodology you trust.

Pricing and availability

DeepSeek publishes clear API pricing for V4.1 Flash-1017 under its `deepseek-flash` alias: $0.15 per million input tokens during off-peak hours and $0.30 during peak hours on cache misses (as low as $0.003-$0.006 on cache hits), and $0.60 to $1.20 per million output tokens depending on time of day, with peak hours defined as 01:00-04:00 and 06:00-10:00 UTC on weekdays. The model is also downloadable and self-hostable under MIT.

Meta does not publish a direct hosted API price for Muse Spark Open; because it is an open-weight release rather than a hosted product, pricing instead comes from the inference providers that host it, including Baseten, Fireworks AI, Groq, Together AI and OpenRouter, each setting its own rates. Developers who want Meta's own hosted infrastructure instead use the smaller, API-based Muse Spark 1.2/1.3 checkpoints through Meta's developer platform, which is a separate commercial product from the open-weight release covered here.

Which one should you actually use?

For teams picking between the two, the decision usually comes down to three questions rather than a single benchmark score. First, licensing: if you plan to offer either model as a hosted API service to third parties at scale, DeepSeek's MIT terms are simpler to clear with legal than Meta's Muse Model License, which requires a separate agreement above a revenue threshold. Second, cost and footprint: DeepSeek V4.1 Flash-1017's smaller active-parameter count (8B/16B versus Muse Spark's 42B) generally makes it cheaper and faster to serve at the same hardware budget, which is reflected in its published API pricing. Third, task mix: the vals.ai and BenchLM data both suggest Muse Spark Open has a real edge on legal and some knowledge-retrieval benchmarks, while DeepSeek V4.1 Flash-1017 tends to lead on terminal and coding-agent benchmarks, so a workload that is coding-heavy may lean DeepSeek while a document- or research-heavy workload may lean Muse Spark. Neither company's benchmark claims should be taken as independently verified; treat vendor-published numbers as a starting point and the aggregator comparisons as the closer approximation of real-world behavior, while recognizing that even those differ by version and methodology. For a wider look at how closed frontier models stack up against each other, see Pandromeda's comparison of Gemini 4 Argon, GPT-6 Astra and Claude Opus 5.5.

What's next

Both companies are expected to keep iterating quickly: DeepSeek has already shipped incremental "point releases" of its Flash line roughly every month since V4.1 Flash's September debut, and its own documentation recommends developers stay current with its DeepSeek Harness tooling to get the full benefit of each update. Meta, for its part, has signaled the Muse family will continue to add open checkpoints alongside its hosted Muse Spark 1.x line, following the smaller 30B Muse Glimmer model it released under Apache 2.0 in August. Expect vals.ai and BenchLM to keep adding head-to-head pages as each side ships new point releases, which means any "winner" claim made today is likely to need revisiting within weeks rather than months. Watching the official Hugging Face model cards and each company's own release notes remains the most reliable way to track what actually changed, since third-party benchmark pages can lag or compare mismatched versions.

Written byTheo Park

Theo Park runs the AI desk at Pandromeda. He follows model launches from the frontier labs and the open-weight community, tracks the assistants and developer tools built on them, and explains what each release changes on pricing, capability and safety. His reporting leans on primary sources: model cards, technical reports, API documentation and the companies' own announcements.

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