diff --git a/CHANGELOG.md b/CHANGELOG.md
index 39dcfa0..7ab9892 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # Changelog
 
+## v1.4.1 (2025-12-05)
+- The new API implements a different scaling value behaviour. To preserve backwards compatibility, this new version 
+aligns with the old behaviour.
+
 ## v1.4.0 (2025-12-05)
 - The October 2025 release of WEO removed bulk downloads and moved everything towards the SDMX API. This update provides a way to parse new releases from the API instead of relying on the XML files. Note that thew new API response does not include observation-level notes or information on when projections start for each country-indicator.
 
diff --git a/pyproject.toml b/pyproject.toml
index 980099e..3d026b6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "imf-reader"
-version = "1.4.0"
+version = "1.4.1"
 description = "A package to access imf data"
 authors = [{ name = "The ONE Campaign" }]
 license = { text = "MIT" }
diff --git a/src/imf_reader/weo/api.py b/src/imf_reader/weo/api.py
index a836ff3..f4c1388 100644
--- a/src/imf_reader/weo/api.py
+++ b/src/imf_reader/weo/api.py
@@ -18,6 +18,13 @@
     9: "Billions",
 }
 
+# Map scale exponents to multipliers (for converting to legacy format)
+SCALE_MULTIPLIERS = {
+    0: 1,
+    6: 1_000_000,
+    9: 1_000_000_000,
+}
+
 # Cache configuration
 _CACHE_DIR = Path.home() / ".cache" / "imf_reader" / "weo"
 _CACHE_TTL = 7 * 24 * 60 * 60  # 7 days in seconds
@@ -148,6 +155,23 @@ def _align_schema(df: pd.DataFrame) -> pd.DataFrame:
     df["LASTACTUALDATE"] = pd.array([pd.NA] * len(df), dtype="Int64")
     df["NOTES"] = pd.array([pd.NA] * len(df), dtype="string")
 
+    # Convert values to match legacy format:
+    # - Legacy format stores OBS_VALUE "in scale" (e.g., 447.416 for 447.416 billion)
+    # - New API returns OBS_VALUE in units (e.g., 447416000000.0)
+    # - Legacy SCALE_CODE is the multiplier (e.g., 1000000000), not the exponent (e.g., 9)
+
+    # First, convert OBS_VALUE from units to "in scale" by dividing by 10^SCALE_CODE
+    # Only apply where SCALE_CODE is present and > 0
+    scale_exponent = pd.to_numeric(df["SCALE_CODE"], errors="coerce")
+    has_scale = scale_exponent.notna() & (scale_exponent > 0)
+    df.loc[has_scale, "OBS_VALUE"] = (
+        pd.to_numeric(df.loc[has_scale, "OBS_VALUE"], errors="coerce")
+        / (10 ** scale_exponent[has_scale])
+    )
+
+    # Convert SCALE_CODE from exponent to multiplier to match legacy format
+    df["SCALE_CODE"] = scale_exponent.map(SCALE_MULTIPLIERS)
+
     # Fix data types to match old parser
     # Numeric columns
     df["OBS_VALUE"] = df["OBS_VALUE"].astype("Float64")