Mistral Large 4 vs Muse Spark 1.2: A 25-Cent Price Gap and a 3-Second Latency One

This is the closest price pairing on the site, and it makes the decision unusually clean. Mistral Large 4 lists at $1.36 per million input tokens and $4.18 output; Muse Spark 1.2 costs $1.25 and $4.25. On input Mistral is 9% dearer; on output it is 2% cheaper. That is a rounding error in both directions, which means price cannot decide this and the other columns have to.

They disagree immediately. Muse returns its first token in 3.03 seconds; Mistral Large 4 is a trillion-parameter MoE with no comparable served measurement, and on the evidence of every model of that class, slower. Muse accepts text, image, video, file and audio; Mistral accepts text and image. Mistral publishes its weights; Muse does not.

The cards

• Mistral Large 4 — $1.36 in, $0.14 cached, $4.18 out; promoted at half, no end date published. 1.05 trillion parameters, 52 billion active, 1.6-billion-parameter vision encoder. 1M context claimed, 524,288 measured. Weights public, 1,060.6 GB bf16, under the Mistral Ecosystem Open-Weight License.

• Muse Spark 1.2 — $1.25 in, $4.25 out; 1M context; input text, image, video, file and audio; output text; 3.03 seconds to first token. No output ceiling is published on the page we read, and no weights.

Two asymmetries are worth naming precisely. Muse takes video, audio and files where Mistral takes pictures and text — a wider input surface, not a higher-resolution one. And Mistral has a measured output window of 524,288 tokens while Muse's maximum output is not stated, which is a gap in the comparison rather than evidence of one.

The one number that is not close

Latency. A 3.03-second first token against a model whose class typically runs several seconds slower is the difference between a product that feels immediate and one that feels deliberate. On a twenty-turn agent loop that is tens of seconds per task; on a chat interface it is the entire perceived quality of the thing.

The counter-argument is volume: Mistral's cached-input line at $0.14 gives a discount on repeated prefixes that Muse's card does not publish. For an agent that re-sends a stable system prompt every turn, that line can be worth more than the 4-second latency gap costs — and it is measurable in an afternoon rather than arguable in a thread.

The licence, again

Mistral's weights are downloadable under a licence that permits commercial use, modification, distribution and further training, reserving only model-as-a-service for a separate agreement. Muse Spark 1.2 has no checkpoint to download. If running the model inside your own perimeter is a requirement, the 25-cent price gap is irrelevant and Mistral wins by existing.

If it is not a requirement, the licence is a column you can ignore, and the decision collapses to latency and input surface.

Per task

One million input tokens and 100,000 output tokens. Muse: $1.25 plus $0.425 — $1.68. Mistral at list: $1.78. On promotion: 89 cents.

So at list the two are 6% apart, and the promotion makes Mistral half the price of Muse. That is the only version of this comparison where the sticker decides anything — and it depends on a discount with no end date.

What to run

Muse Spark 1.2 is on OrcaRouter at its vendor's published rate, on one OpenAI-compatible endpoint with automatic failover, alongside 223 other models. Mistral Large 4 is not in that catalogue; it runs through Mistral's own API and third-party hosts.

The routing rule: audio and video input to Muse, image and text work to whichever of the two your own measurement says completes a task in fewer tokens. At a 2% price gap, that measurement is the only thing left that can settle it.