diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index 146eabce23..fec2b01802 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -33,7 +33,7 @@ enum resize_algo {
     RESIZE_ALGO_BILINEAR, // stretch to target resolution
     RESIZE_ALGO_BICUBIC, // center-crop when aspect ratio doesn't match
     RESIZE_ALGO_BICUBIC_PILLOW,
-    // RESIZE_ALGO_LANCZOS, // TODO
+    RESIZE_ALGO_LANCZOS,
 };
 
 // Padding style for img_tool::resize
diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index 36cd463b20..72d35fce69 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -68,6 +68,9 @@ struct img_tool {
                 case RESIZE_ALGO_BICUBIC_PILLOW:
                     resize_bicubic_pillow(src, dst, target_resolution.width, target_resolution.height);
                     break;
+                case RESIZE_ALGO_LANCZOS:
+                    resize_lanczos_pillow(src, dst, target_resolution.width, target_resolution.height);
+                    break;
                 default:
                     throw std::runtime_error("Unsupported resize algorithm");
             }
@@ -97,6 +100,9 @@ struct img_tool {
                 case RESIZE_ALGO_BICUBIC_PILLOW:
                     resize_bicubic_pillow(src, resized_image, new_width, new_height);
                     break;
+                case RESIZE_ALGO_LANCZOS:
+                    resize_lanczos_pillow(src, resized_image, new_width, new_height);
+                    break;
                 default:
                     throw std::runtime_error("Unsupported resize algorithm");
             }
@@ -337,22 +343,50 @@ private:
         }
     }
 
-    // Bicubic resize function using Pillow's ImagingResample algorithm
+    // Pillow-compatible separable resampling (Bicubic and Lanczos)
     // Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c
     //
-    // Key Difference with resize_bicubic:
-    // 1. Uses separable filtering: horizontal pass followed by vertical pass
+    // Key properties:
+    // 1. Separable filtering: horizontal pass followed by vertical pass
     // 2. Pre-computes normalized filter coefficients for each output pixel
-    // 3. Applies convolution using fixed-point integer arithmetic for performance
+    // 3. Fixed-point integer arithmetic (22 fractional bits) for speed and determinism
     static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
+        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/false);
+    }
+
+    // Lanczos-3 (support radius 3), matches Pillow's Image.LANCZOS
+    static bool resize_lanczos_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
+        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/true);
+    }
+
+    static bool resize_pillow(
+            const clip_image_u8 & img,
+            clip_image_u8 & dst,
+            int target_width,
+            int target_height,
+            bool use_lanczos) {
         // Fixed-point precision: 22 bits = 32 (int32_t) - 8 (uint8_t pixels) - 2 (headroom for accumulation)
         // This allows encoding fractional weights as integers: weight * 2^22
         const int PRECISION_BITS = 32 - 8 - 2;
 
-        // Bicubic filter function with a = -0.5 (Note that GGML/PyTorch takes a = -0.75)
+        // Resample filter: Lanczos-3 (support [-3, 3]) or bicubic with a = -0.5 (support [-2, 2])
+        // Note: GGML/PyTorch bicubic uses a = -0.75, Pillow uses a = -0.5
         // Returns filter weight for distance x from pixel center
-        // Support: [-2, 2], meaning the filter influences pixels within 2 units of distance
-        auto bicubic_filter = [](double x) -> double {
+        auto resample_filter = [use_lanczos](double x) -> double {
+            if (use_lanczos) {
+                if (-3.0 <= x && x < 3.0) {
+                    auto sinc = [](double v) {
+                        if (v == 0.0) {
+                            return 1.0;
+                        }
+                        const double pi_v = v * 3.141592653589793238462643383279502884;
+                        return std::sin(pi_v) / pi_v;
+                    };
+                    return sinc(x) * sinc(x / 3.0);
+                }
+                return 0.0;
+            }
+
             constexpr double a = -0.5;
             if (x < 0.0) {
                 x = -x;
@@ -366,8 +400,8 @@ private:
             return 0.0;  // Zero outside [-2, 2]
         };
 
-        // Filter support radius: bicubic extends 2 pixels in each direction
-        constexpr double filter_support = 2.0;
+        // Filter support radius: 2 for bicubic, 3 for lanczos
+        const double filter_support = use_lanczos ? 3.0 : 2.0;
 
         // Clipping function for 8-bit values
         auto clip8 = [](int val) -> uint8_t {
@@ -434,7 +468,7 @@ private:
                 // Compute filter weights for each contributing input pixel
                 for (x = 0; x < xmax; x++) {
                     // Distance from input pixel center to output pixel center in input space
-                    double w = bicubic_filter((x + xmin - center + 0.5) * ss);
+                    double w = resample_filter((x + xmin - center + 0.5) * ss);
                     pre_weights[xx * ksize + x] = w;
                     ww += w;  // Accumulate for normalization
                 }
@@ -463,6 +497,12 @@ private:
             const double fxp_scale = std::ldexp(1.0, PRECISION_BITS); // 1.0 * 2^PRECISION_BITS
 
             for (int i = 0; i < outSize * ksize; i++) {
+                if (use_lanczos) {
+                    // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice
+                    const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5);
+                    weights[i] = static_cast<int32_t>(rounded);
+                    continue;
+                }
                 double tmp_val = pre_weights[i] * fxp_scale;
                 if (pre_weights[i] < 0) {
                     tmp_val -= 0.5;