How Output Float Changes Trade-Up Profit

Most explanations of CS2 trade-ups treat output float as a detail you check at the end. It is not a detail. For boundary-hugging contracts — which is where almost all the profit lives — the output float is the single biggest lever on what your result is worth. A change too small to see can multiply or erase your profit.

This post puts real numbers on that claim. To follow along on live contracts, browse current trade-ups or test inputs in the calculator.

Float decides condition, condition decides price

A trade-up's output float is deterministic. You can compute it before you buy anything:

output_float = avg_adjusted_input_float * (output_max - output_min) + output_min

That float lands the output in exactly one wear condition, and CS2's condition cutoffs are hard. The Factory New / Minimal Wear line sits at 0.07. An output at 0.069 is Factory New; at 0.071 it is Minimal Wear. Nothing about the skin looks different. The price is a different story.

The data: one boundary, huge price gaps

We pulled per-condition prices for real CS2 skins from live marketplace data (CSFloat sales, captured June 2026) and compared the Factory New price to the Minimal Wear price for the same skin — the value of crossing that single 0.07 boundary:

Every one of these crosses a 10x value line at float 0.07. The M4A1-S | Nightmare alone swings $215 across a float change you could not detect by looking at the weapon. If a trade-up's output lands at 0.069 instead of 0.071, that is the difference between the two columns.

Why this breaks naive profit estimates

The condition itself is computable: the deterministic float tells you whether the output is Factory New or Minimal Wear before you buy. The problem is the price you attach to it. A model that values the output at a blended condition price throws away exactly the information that matters most near 0.07, where value jumps. Price a boundary output as a blended average and your profit estimate is off by the multipliers above, not by a few percent. The contracts most worth doing are the ones engineered to sit closest to a boundary, which is precisely where a blended price is least trustworthy.

It is not only the boundary, either. Within a single condition, price still drifts with float: a 0.16 Field-Tested generally outsells a 0.37 Field-Tested. The boundary effect is the dramatic one, but intra-condition float still moves money on the margin.

How the float actually gets calculated

You control the output float through your input selection. Each input contributes its adjusted float — its position within its own min-to-max range — and the average of those adjusted values maps onto the output skin's range. Swap one high-float input for a lower one and the predicted output float drops, sometimes just enough to cross a boundary in your favor. That is the whole game on boundary contracts: nudging the average until the deterministic output lands one tick inside the better condition.

How to use this

Before buying inputs, predict the exact output float and check which condition it lands in — and how close it is to the next boundary. A contract that lands at 0.068 is fragile: a single slightly-worse input can tip it into Minimal Wear and collapse the value. A contract that lands at 0.045 has margin to spare. Price the output at that exact float, not at a blended condition average, and your profit estimate will be far closer to the actual sale range — and far less likely to be distorted near a boundary.

Run your inputs through the calculator to see the predicted output float and value, compare live contracts, or read why CS2 trade-up calculators disagree for the pricing mechanics behind these numbers.

Method: per-condition prices captured from TradeUpBot's live marketplace data (CSFloat sales, with reference pricing as fallback) on 2026-06-24. Prices move with the market; the ratios are a dated snapshot of the boundary effect, not a fixed quote.

Published 2026-06-24 by TradeUpBot Team.