DeepSeek V4 Pro (Non-reasoning) vs Muse Spark 1.2 (xhigh): The Ultimate Performance & Pricing Comparison

Muse Spark 1.2 (xhigh) is the stronger measured model, while DeepSeek V4 Pro (Non-reasoning) is the lower-cost operational choice, according to Artificial Analysis.

Muse posts a 56.8 intelligence index, compared with 31.9 for DeepSeek. It also leads on every shared evaluated measure in this brief: GPQA, HLE, SciCode, and LCR. That makes Muse the better default candidate where answer quality, difficult research questions, and broad task completion drive product value.

DeepSeek changes the decision when requests are frequent, outputs are visible to users, or budget limits model coverage. Its blended price is $0.544 per 1M tokens, versus $2 for Muse. DeepSeek also reports 62.894 median output tokens per second. The supplied Muse speed field is 0, so it cannot establish that Muse is slow. It establishes only that the brief does not provide a usable Muse throughput measurement.

The important catch is deployability. DeepSeek's official documentation currently maps the stable deepseek-v4-pro alias to DeepSeek-V4-Pro-0813, not the 0424 non-reasoning release in this comparison. DeepSeek's pricing documentation does not confirm that the exact 0424 identifier remains callable. Meta's AI documentation does not list Muse Spark 1.2 or its API alias. Treat this as a selection between measured records first, then validate access before committing production work.

Data provided by Artificial Analysis.

Muse Spark 1.2 (xhigh) is the better quality-first choice, but its official availability is less evidenced than DeepSeek's current platform presence.

The clearest measured difference is general capability. Muse reaches 56.8 on the intelligence index, while DeepSeek reaches 31.9. Muse also records 0.904 on GPQA, 0.455 on HLE, 0.564 on SciCode, and 0.833333333333333 on LCR. DeepSeek records 0.717, 0.082, 0.424, and 0.496666666666667 on those same measures. Artificial Analysis supports the direction of that choice, but benchmarks do not prove a model will fit your repository, prompts, tool definitions, or safety requirements.

DeepSeek has a more concrete current vendor path, but that path is version-shifted. DeepSeek's documentation lists OpenAI-compatible and Anthropic-compatible endpoints for its current stable alias. It also describes JSON output and tool calling for the current 0813-mapped alias. Those facts cannot be assumed for the 0424 non-reasoning record.

Muse has the opposite problem. Meta's documentation describes Llama-family access, yet does not name Muse Spark 1.2. Therefore, the performance record is useful for ranking capability, but insufficient for procurement or integration approval.

Muse Spark 1.2 (xhigh) should reduce quality risk on demanding tasks because it leads DeepSeek across every shared evaluation in the supplied data.

The chart below shows the score gaps, so the decision is about consequences rather than repeating the values. Muse's lead is most meaningful when a weak answer causes downstream work: an incorrect technical plan, an unsupported scientific claim, a brittle code change, or an incomplete long-document synthesis. In those cases, a model that needs fewer retries can be worth a higher token rate. Artificial Analysis reports Muse ahead on GPQA, HLE, SciCode, and LCR, which gives it a broader measured quality case than a single benchmark would.

DeepSeek remains a sensible choice for bounded work. Fast classification, extraction, short transformations, structured drafts, and human-reviewed generation can tolerate a lower benchmark ceiling. Its reported 62.894 median output tokens per second also favors interactive surfaces where users wait for visible text. The 1.24 latency value is useful only as DeepSeek's reported measurement. Muse has 0 for both supplied speed and latency fields, so no responsible speed ranking can be made from latency.

Several high-value questions remain unanswered. The brief has no shared coding benchmark because DeepSeek's coding index is null while Muse's is 72.2. It also lacks shared LiveCodeBench results. That means you should not claim that Muse will write better production code for your stack based solely on this comparison. Build a small acceptance set from your real tasks, including tool calls, invalid inputs, repository conventions, and regression fixes. Compare pass quality, review effort, and retry behavior before routing production traffic.

Neither official source supplies a model-specific failure catalog. DeepSeek's documentation documents current product features, and Meta's documentation does not document Muse Spark 1.2. Evidence is therefore insufficient to rank hallucination behavior, multilingual quality, vision support, or tool-use reliability.

DeepSeek V4 Pro (Non-reasoning) is the lower-cost option at $0.544 blended pricing, versus $2 for Muse Spark 1.2 (xhigh).

The chart below already presents the token prices. The operational interpretation is more important: DeepSeek is attractive when your workload is high-volume and answers are short, constrained, or reviewed. A lower output price matters especially for chat interfaces, agent logs, generated documentation, and repeated transformations. DeepSeek also has lower listed input and output prices in the supplied data. Artificial Analysis is the source for those comparison values.

A lower token price does not guarantee a lower delivered-task cost. DeepSeek becomes more expensive in practice if lower answer quality triggers repeated prompting, human correction, failed tool calls, or escalation to another model. Muse can justify its higher listed rate when a correct first response avoids those costs. The supplied materials do not contain retry rates, task completion rates, tool-call success rates, or human-review time. Those missing measures are the main cost uncertainty, not a minor footnote.

Version drift adds another budget risk for DeepSeek. DeepSeek's pricing documentation states current prices for the stable deepseek-v4-pro alias, which maps to 0813. It does not establish pricing for deepseek-v4-pro-0424-non-reasoning. The same page announces peak and off-peak pricing changes, so a team must verify the active model and active price before estimating spend.

Muse has a different verification gap. Meta's documentation does not publish a Muse Spark 1.2 price or callable entry in the supplied evidence. Use the comparison price for scenario analysis, but obtain the actual commercial route and billing terms before treating it as a purchase commitment.

Muse Spark 1.2 (xhigh) is the recommended primary candidate for quality-critical development workflows because its 56.8 intelligence index exceeds DeepSeek's 31.9.

Use Muse first for tasks where a wrong result is costly: difficult implementation planning, complex debugging hypotheses, research-heavy product work, and long-context analysis. The shared benchmarks give Muse a consistent lead, rather than a lead confined to one type of evaluation. Artificial Analysis supports that performance recommendation. Still, it does not prove official availability, context capacity, output limits, or API behavior for Muse Spark 1.2.

Use DeepSeek V4 Pro (Non-reasoning) for cost-sensitive and latency-sensitive routes after verifying the exact deployment target. Its $0.544 blended price and reported 62.894 output tokens per second make it compelling for routine generation, extraction, templated customer support drafts, and human-in-the-loop workflows. The model can be a strong secondary route when the task has clear validation rules and failure recovery is cheap.

Do not route production traffic merely from the names in this comparison. Meta's documentation does not list Muse Spark 1.2, so its endpoint, parameters, pricing route, and limits remain unverified. DeepSeek's documentation lists current platform details, but confirms that its stable alias now resolves to 0813 rather than the compared 0424 release.

Make the decision with three checks. First, confirm that each exact model identifier can be purchased and called. Second, run your own test set with representative code, tools, prompts, and review criteria. Third, route based on business cost: choose Muse where quality failures are expensive, and DeepSeek where volume and response pace matter more. This two-route design is only appropriate if your team can measure outcomes and maintain fallback behavior.

DeepSeek V4 Pro (Non-reasoning) and Muse Spark 1.2 (xhigh) both require an access and integration check before production approval.

The data is strong enough to rank selected measured capability and listed token economics. It is not strong enough to answer several questions that developers normally need answered before building. Neither model has a confirmed context window in the data brief. Neither supplied official source confirms multimodal support for the exact compared record. Neither source provides a reliable community evidence base for coding experience, speed perception, or known failure patterns.

DeepSeek has partial current-platform evidence. DeepSeek's documentation documents JSON output, tool calling, Responses API, Anthropic API compatibility, Chat Prefix Completion, and FIM Completion for the current stable alias. It also says FIM Completion is available only in non-thinking mode. Because that alias maps to 0813, these capabilities should not be copied into a 0424 requirements document without direct confirmation.

Muse has no equivalent supplied product evidence. Meta's documentation covers Llama models and acquisition paths, but does not name Muse Spark 1.2. This is not evidence that Muse cannot be used. It is evidence that the provided material cannot verify its use.

Ask vendors or platform owners for the exact endpoint, model identifier, region availability, pricing schedule, input limit, output limit, structured-output behavior, tool-call contract, retention terms, and support path. Then test those claims with your own account. Until that happens, benchmark leadership should influence the shortlist, not replace technical due diligence.