---
title: How does Matecat calculate payable words?
description: Matecat relies on advanced technology, utilizing Translation Memories (TMs), Machine Translation (MT), and infra-text analysis to leverage matches and pre-translate raw content. Matecat supports a com
---

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# How does Matecat calculate payable words?

Matecat uses advanced technology, leveraging **Translation Memories (TMs)**, **Machine Translation (MT)**, and infra-text analysis to pre-translate raw content efficiently.  
Matecat supports both [private TMs](https://guides.matecat.com/importing/exporting?hsLang=en) and [MyMemory](https://mymemory.translated.net/), the world's largest public TM.

Segments without matches in the private or public TMs are pre-translated using Machine Translation.

Freelancers’ earnings are determined by per-word rates and workload. Matecat provides **payable word breakdowns** for each project so translators can make informed decisions about their work. You can access this information in the [Volume Analysis Report](https://guides.matecat.com/volume-analysis-page?hsLang=en).  
Payable words are calculated by multiplying the word count of each analysis bucket by its corresponding payable rate and are marked in **bold.**

![](https://guides.matecat.com/hs-fs/hubfs/undefined-Nov-09-2026-03-28-59-7467-PM.png?width=670&height=182&name=undefined-Nov-09-2026-03-28-59-7467-PM.png)

Example calculation:

(2 \* 0.6) + (3 \* 0.6) + (169 \* 0.67) = 116

The table above reads as follows:

**Payable Words (Weighted):** the sum of the raw word count for each analysis bucket multiplied by its payable rate (116 in the table above). In the new interface, you can see both the Raw count and the resulting Weighted count broken down row by row.

**Total Words:** the raw word count before leveraging any content from translation memory matches, repetitions, or machine translation (174 in the table above, displayed in the rightmost column). This matches the standard word count given by regular text editors.

**New Words:** all words found in segments that:

- Do not match any fuzzy or complete match in private TM keys or the public TM;
- Are not repeated within the project;
- Do not have an MT suggestion.

**Repetitions:** the number of words in identical segments occurring more than once in the project (0 in the table above).

For example, imagine that we find the following source segments:

1. *My house is blue.*
2. *My house is blue.*

Segments 1 and 2 are identical 4-word segments. Segment 1 is weighted based on any matches in TM or MT suggestions. Segment 2 counts as 4 repeated words in our volume analysis. Since repetitions are weighted at 30% of the raw word count, segment 2 will count as 1.2 words toward the final payable word count.

**Internal Matches:** similar to repetitions, internal matches identify segments that repeat throughout the document, but with slight differences (0 in the example above).

Imagine the following source segments:

1. *My house is blue.*
2. *My house is red.*

Matecat recognizes that segment 2 is similar to segment 1 (3 out of 4 words identical, or a 75% match). Segment 1 is weighted based on any matches in TM or MT suggestions. Segment 2 is considered an internal partial match (75%–99%). Since internal partial matches are weighted at 60% of the raw word count, segment 2 counts as 2.4 words toward the final payable word count (4 \* 0.6).

**Partial TM matches:** While internal matches look at similarities within the document, partial TM matches check the document against all translation memories associated with the project in "lookup" mode. All matches below 100% are partial matches ("fuzzy matches"):

- **TM Partial 50-74%:** Lower fuzzy range, paid at 100%.
- **TM Partial 75-84%, 85-94%, and 95-99%:** Higher fuzzy ranges, paid at 60%.  
   In the example above, we have 2 raw words in the 85–94% range (weighted as 1 word) and 3 raw words in the 95–99% range (weighted as 2 words).

**TM 100%:** a 100% match between a segment to be translated and an identical segment found in a private translation memory. While the translation suggested is probably accurate, these segments are weighted at 30% of the raw word count because the suggestion could require minor adjustments for context (0 words in the example above).

**Public TM 100%:** a 100% match between a segment to be translated and an identical segment in the public translation memory (MyMemory). These segments are also weighted at 30% of the raw word count (0 words in the example above).

**101% Matches:** an in-context exact match, indicated by the **TM 101%** column in the Volume Analysis report (0 words in the example above).

How a 101% match is identified:

Each time a Translation Memory is updated, it stores key metadata alongside each translated segment:

- The source segment and its target translation;
- The preceding and following source segments.

For a segment to qualify as a 101% match, Matecat must identify three identical segments (100% matches) in the exact same sequence.

*Example:*

Imagine a document contains these three consecutive segments:

1. *My house is blue.*
2. *My house is red.*
3. *My house is green.*

Matecat identifies a 100% match for segment 2. The stored metadata shows that the preceding segment in the memory is also an exact match for segment 1, and the subsequent segment is an exact match for segment 3. Because the surrounding context is verified, Matecat confirms this as a **101% match**.

![](https://guides.matecat.com/hs-fs/hubfs/undefined-Nov-09-2026-03-28-59-4878-PM.png?width=670&height=226&name=undefined-Nov-09-2026-03-28-59-4878-PM.png)

When a segment is a 101% match:

- It appears pre-translated and pre-approved by Matecat, marked with a green status indicator and a padlock icon.
- The payable rate is 0%, meaning you do not need to translate or approve it.
- While you do not need to retranslate it, the segment remains visible to provide context and maintain text flow.
- If you need to make changes, you can simply click the padlock icon to unlock the segment, edit the text, and confirm it again.

 

### **TM Payable Rates**

According to industry standards, translation memory matches are weighted as follows:

- Lower fuzzy ranges (TM Partial 50%–74%): Paid at 100% of their raw word count.
- Higher fuzzy ranges (TM Partial 75%–84%, 85%–94%, 95%–99%): Paid at 60% of their raw word count.
- Exact matches (TM 100% and Public TM 100%): Paid at 30% of their raw word count.
- 101% matches: Paid at 0% (no translation required).

### **MT Payable Rates**

For Machine Translation (MT) suggestions, payable rates are dynamically adjusted based on real post-editing data: Post-Editing Effort (PEE) and Time to Edit (TTE).

Our data shows that linguists spend a similar amount of time editing MT suggestions as they do editing higher fuzzy match ranges: on average, it takes 1.59 seconds per word to edit MT suggestions, while it takes 1.64 seconds per word to edit 85%–94% fuzzy matches. The quality of MT depends on the language combination and the volume of corrections fed into the engine, as does the average TTE. For this reason, payable rates applied to MT change according to the language combination, and they are updated as MT performance changes over time.

In the example above (English to Finnish), the applied MT payable rate is 67%, resulting in 113 weighted words out of 169 raw words.

**Table of Default Payable Rates**

| New | 100% |
| --- | --- |
| TM Partial 50%-74% | 100% |
| Machine Translation | 72%-82% |
| TM Partial 75%-99% | 60% |
| Internal Matches (75%-99%) | 60% |
| Repetitions | 30% |
| TM 100% | 30% |
| Public TM 100% | 30% |
| TM 101% | 0% |

 

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