Muse Wonder vs DeepSeek V4 Pro: $4.52 Per Task Against $0.67, and 16 Index Points Between Them

The comparison between Muse Wonder and DeepSeek V4 Pro is not really a model comparison. It is a price-per-task comparison with a capability gap attached. On Artificial Analysis' independent measurements, DeepSeek V4 Pro lands at about $0.67 per Intelligence Index task on the DeepSeek V4 Pro API at $1.32 per million input and $3.96 per million output tokens. Muse Wonder (max) lands at about $4.52 per index task at $4.00 in and $20.00 out. That is roughly 6.7 times the cost per unit of measured capability, against a raw index score of 36 for DeepSeek V4 Pro and 52 for Muse Wonder.

So the question is not which model is better. It is whether sixteen index points and a multimodal input path are worth nearly seven times the run rate — and for most workloads that ship a lot of tokens, the arithmetic says no. Here is where that arithmetic breaks down and where it holds.

The arithmetic that actually decides this

Both figures come from Artificial Analysis, measured on the same index, so they are directly comparable in a way vendor scoreboards are not:

• Artificial Analysis Intelligence Index — Muse Wonder (max) 52, ranked #9 of 219; DeepSeek V4 Pro 0813 (max) 36, ranked #9 of 117

• Cost per index task — Muse Wonder about $4.52; DeepSeek V4 Pro about $0.67

• List price — Muse Wonder $4.00 in / $20.00 out per million tokens; DeepSeek V4 Pro $1.32 in / $3.96 out on its own API

• Blended price on a 7:2:1 cache-hit/input/output mix — DeepSeek V4 Pro about $0.69 per million tokens; Muse Wonder about $2.94

• Cache discount — DeepSeek V4 Pro 97%; Muse Wonder roughly 95%

• Output tokens consumed in evaluation — DeepSeek V4 Pro about 160 million; Muse Wonder about 130 million

• Output speed — DeepSeek V4 Pro about 91.7 tokens per second; Muse Wonder not yet published by the same lab

• Context window — both 1,048,576 tokens

• Modal inputs — Muse Wonder takes text, image, speech, video, and files; DeepSeek V4 Pro is text only

• Weights — DeepSeek V4 Pro is an open-weights mixture-of-experts model under MIT, 1.6 trillion total parameters with 49 billion active; Muse Wonder is proprietary

The index numbers look closer than they are in practice, because the ranking denominators differ. Muse Wonder's ninth place is out of 219 models; DeepSeek V4 Pro's ninth place is out of 117. Same rank number, very different position in the field.

What $0.67 a task buys you

DeepSeek V4 Pro shipped on August 13, 2026, which means it has had two months of production use and independent measurement. That maturity shows up in the numbers that are not benchmark scores: a 97% cache discount, a published blended price, a measured output speed, and a per-task cost estimate — the four things you need to build a budget model. Muse Wonder has none of those beyond the per-task figure, because it is a day old.

It is also text-only, and that is a genuine constraint rather than a footnote. If your pipeline ingests audio or video, Muse Wonder collapses a transcription step and a reasoning step into one call; DeepSeek V4 Pro requires you to pipe the transcript in as text. Whether that matters is entirely a function of your input modality — for a code assistant or a document pipeline it never comes up.

The open-weights licence is the other half of the value proposition, and it is the thing Muse Wonder cannot match at any price. MIT weights at 1.6 trillion total and 49 billion active parameters means a team with the hardware can run this model on its own infrastructure, with no per-token bill, no rate limit, and no vendor that can deprecate the checkpoint. For a workload with steady high volume and a compliance requirement that data never leaves the building, that is frequently the whole decision — and the API price above is then just the cheapest way to prototype before you buy GPUs.

What the extra $3.85 per task buys you

Sixteen index points is not nothing. It is the difference between a model that ranks in the middle of the current frontier and one that ranks in the top ten, and on agentic work the gap tends to widen rather than narrow because errors compound across tool calls.

Meta's own vendor-reported table puts Muse Wonder's strengths in the same place: Terminal-Bench 4.0-AA at 85.6, SWE-Marathon at 78.8, OSWorld V2.1 at 72.6 for computer use, and 60.5 on AutomationBench-AA. Those are long-running agent suites where a model has to hold a plan across many steps, and Meta reports Muse Wonder competitive with Claude Opus 5.5 and GPT-6 Astra on several of them. All of it is vendor-reported and none independently reproduced — the same caveat that applies to every launch table.

The capability that is not on any of those benchmarks is memory. Muse Wonder ships with a feature Meta calls Active Memory, persistent state across a session, plus Active Focus for attention management on long tasks. DeepSeek V4 Pro's 1M context is a window you manage yourself; if Meta's implementation delivers, it is a different kind of capability rather than a bigger number. It is also the claim least likely to be verifiable from a model card, and the one to test hardest before paying for.

There is a cost-side argument for Muse Wonder that has nothing to do with quality: mandatory reasoning at an effort ladder from minimal to max, defaulting to medium. That is not a discount, but it is a dial. Running Muse Wonder at minimal or low on classification and extraction traffic keeps the $20-per-million output rate from applying to tokens that did not need to be spent. The trap is leaving everything at the default on a workload where a cheaper model would have passed.

Where the two models stop competing

At some point the comparison stops being about a price difference and becomes about licence and control. DeepSeek V4 Pro's weights are downloadable under MIT. That means fine-tuning, that means quantised deployment on your own hardware, and that means a checkpoint that keeps working if the vendor changes its terms. Nothing Muse Wonder offers substitutes for that, and the $0.67-per-task figure is really the entry point to a self-hosting decision rather than the end of one.

Conversely, if you need speech and video as first-class inputs, or persistent cross-session memory as a managed feature rather than something you build, DeepSeek V4 Pro's architecture does not reach those at any price. Those are the two ends of the spectrum where this stops being a cost comparison.

Both are reachable from one endpoint

DeepSeek V4 Pro is on OrcaRouter. Both the August 13 checkpoint that Artificial Analysis evaluated and the earlier V4 Pro build are listed at $0.66 per million input tokens and $1.98 per million output, with a cache-read rate of $0.022. Those are the company's own rates passed through without markup, which is why they sit below the $1.32/$3.96 figure Artificial Analysis used: the per-task cost above is calculated on its standard API pricing, and a cheaper route to the same checkpoint changes the left side of the comparison without changing the model.

Muse Wonder is not on OrcaRouter. There is no meta/muse-wonder entry in our catalogue and this article will not imply one exists — Meta's own API and Muse Code are where it runs today. What we do carry from Meta Superintelligence Labs is Muse Spark 1.2 and Muse Spark 1.1, so the family has a route even though this release does not.

The reason that matters for this particular decision is that the $4.52-versus-$0.67 spread is large enough that it should be tested on your own traffic before it is believed. OrcaRouter serves 200-plus models on one OpenAI-compatible endpoint with provider list prices passed through unchanged, so a benchmark harness built against DeepSeek V4 Pro can be pointed at a second model by changing a string, and a vendor price cut shows up the same day rather than at the next billing cycle. For a comparison where the ratio is the whole argument, the cheapest thing you can do is measure your own task mix.

The call

• Default to DeepSeek V4 Pro for high-volume, text-only, cost-sensitive work. At $0.67 per index task against $4.52, it has to lose badly on your specific task before the cheaper model is the wrong answer, and it carries an open-weights licence Muse Wonder cannot offer at any price.

• Move specific workloads to Muse Wonder when the task is long-horizon and agentic, when the input includes audio or video, or when persistent cross-session memory is worth paying a managed price for.

• Do not pay for Muse Wonder at default effort on traffic that a cheaper model passes. The effort ladder is the only cost lever the model gives you and it is worth using.

• Expect the 16-point gap to matter more than the index suggests on multi-step agents and less than it suggests on single-shot extraction, classification, and summarisation.

Nearly seven times the cost for sixteen index points is a bad trade for the average workload and a good trade for a specific one. The spread is wide enough that you can find out which side you are on for the price of an afternoon of A/B testing.