GEO Glossary

Active Parameters

Active parameters are the part of a mixture-of-experts model used for each token. Why a 1-trillion-parameter model like Mistral Large 4 runs with about 49B active, and why memory still depends on the total.

By Ramanath, CTO & Co-Founder at Presenc AI · Last updated: November 2, 2026

Definition

Active parameters are the weights a model actually uses to produce each token. In a dense model, every parameter is used every time, so active equals total. In a mixture-of-experts (MoE) model, a small router picks a few "experts" for each token and skips the rest, so only a fraction of the weights do work on any given step. Model names now often carry both numbers. Mistral's Hugging Face repo for Large 4 is Mistral-Large-4-1T-A52B: about 1 trillion total, about 52 billion active.

Recent Examples

ModelTotalActive per tokenReleased
Mistral Large 4About 1T49B (52B counting embedding and output layers)Open weights October 29, 2026
Muse Spark Open (Meta)816B42BOctober 15, 2026
Reflection Beam501B23BWeights October 16, 2026
Gemma 4 26B-A4B (Google)26B4B2026

Mistral's model card describes 128 routed experts with 4 active per token plus 1 shared expert. Coverage disagrees on the exact figures (1T or 1.05T total, 49B or 52B active), mostly because of what is counted.

What Each Number Controls

  • Active parameters set speed and compute cost. Fewer active weights per token means less arithmetic and less memory read per token, so a 1T MoE can generate faster than a dense 400B model.
  • Total parameters set memory. Every expert must be loaded, because the router can pick any of them. A 1T model needs memory for all 1T weights, at whatever precision you run it.

That second point is the one people miss. A model with "only 42B active" still won't fit on a 128 GB laptop if it has 816B total. See how much RAM each open-weight model needs and running Mistral Large 4 locally.

Why It Matters for AI Visibility

Cheap active compute is why very large open models now run inside companies, national clouds and agent products at low prices. Their answers are not observable from outside, so what they learned about your brand at training time is what those users get. Size also affects knowledge: larger total parameter counts tend to retain more long-tail facts, which includes smaller brands.

Frequently Asked Questions

It means about 52 billion active parameters per token. Mistral-Large-4-1T-A52B is a mixture-of-experts model with about 1 trillion total parameters, of which about 52 billion (49 billion excluding embedding and output layers) are used for each token.
No. Memory depends on total parameters, because all the experts have to be loaded. Active parameters mainly determine speed and compute per token.
Routing each token to a few experts lets a model hold far more knowledge than it uses per step, so it can be large in total but cheap to run per token.
Often on knowledge and hard tasks, but not always, and it needs far more memory. Compare benchmark results for the tasks you care about rather than parameter counts.

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