diff --git a/gateway/platforms/api_server.py b/gateway/platforms/api_server.py
index 628713224..415f3b2dc 100644
--- a/gateway/platforms/api_server.py
+++ b/gateway/platforms/api_server.py
@@ -362,6 +362,159 @@ def _multimodal_validation_error(exc: ValueError, *, param: str) -> "web.Respons
     )
 
 
+def _is_coder_model(model_name: Optional[str]) -> bool:
+    """Return True if model_name looks like a coder model (case-insensitive 'coder' in name)."""
+    if not model_name:
+        return False
+    return "coder" in str(model_name).lower()
+
+
+def _extract_image_urls_from_messages(messages: List[Dict[str, Any]]) -> List[str]:
+    """Extract all image URLs from OpenAI-style messages payload."""
+    image_urls: List[str] = []
+    for msg in messages:
+        if not isinstance(msg, dict):
+            continue
+        content = msg.get("content")
+        if not content:
+            continue
+        # Normalize multimodal content to list of parts
+        parts: Any = content
+        if isinstance(content, str):
+            continue  # text-only message has no images
+        if not isinstance(parts, list):
+            continue
+        for part in parts:
+            if not isinstance(part, dict):
+                continue
+            part_type = str(part.get("type", "")).strip().lower()
+            if part_type in {"image_url", "input_image"}:
+                img_ref = part.get("image_url")
+                if isinstance(img_ref, dict):
+                    url = img_ref.get("url")
+                else:
+                    url = img_ref
+                if isinstance(url, str) and url.strip():
+                    image_urls.append(url.strip())
+    return image_urls
+
+
+async def _maybe_enrich_with_vision_if_coder(
+    body: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    For coder model requests with image attachments, pre-analyze images
+    with the vision fallback model and replace image parts with descriptive text.
+
+    Returns a modified copy of body with image URLs replaced by text
+    descriptions in the messages array.
+    """
+    model_name = body.get("model")
+    if not _is_coder_model(model_name):
+        return body
+
+    messages = body.get("messages")
+    if not messages or not isinstance(messages, list):
+        return body
+
+    image_urls = _extract_image_urls_from_messages(messages)
+    if not image_urls:
+        return body
+
+    # Import here to avoid circular dependency issues
+    try:
+        from tools.vision_tools import vision_analyze_tool
+    except ImportError:
+        logger.warning(
+            "Vision tool not available for coder model enrichment. "
+            "Install hermes-agent tools to enable this feature."
+        )
+        return body
+
+    # Use the configured vision model or fall back to VL-30B
+    # We'll let vision_analyze_tool use its own default (auxiliary.vision)
+    # but we can optionally override by passing model parameter
+    # For now, let it auto-resolve via config
+    analysis_prompt = (
+        "Describe everything visible in this image in thorough detail. "
+        "Include any text, code, data, objects, people, layout, colors, "
+        "and any other notable visual information."
+    )
+
+    # Build a mapping of URL -> description
+    url_to_desc: Dict[str, str] = {}
+    for img_url in image_urls:
+        try:
+            result_json = await vision_analyze_tool(
+                image_url=img_url,
+                user_prompt=analysis_prompt,
+            )
+            result = json.loads(result_json)
+            if result.get("success"):
+                desc = result.get("analysis", "")
+                url_to_desc[img_url] = (
+                    f"[The user sent an image~ Here's what I can see:\n{desc}]\n"
+                    f"[If you need a closer look, use vision_analyze with image_url: {img_url} ~]"
+                )
+            else:
+                url_to_desc[img_url] = (
+                    "[The user sent an image but I couldn't quite see it this time (>_<) "
+                    f"You can try looking at it yourself with vision_analyze using image_url: {img_url}]"
+                )
+        except Exception as e:
+            logger.error("Vision auto-analysis error for image %s: %s", img_url, e)
+            url_to_desc[img_url] = (
+                f"[The user sent an image but something went wrong when I tried to look at it~ "
+                f"You can try examining it yourself with vision_analyze using image_url: {img_url}]"
+            )
+
+    # Now replace image parts with text in a copy of messages
+    enriched_messages: List[Dict[str, Any]] = []
+    for msg in messages:
+        msg_copy = dict(msg)
+        content = msg_copy.get("content")
+        if isinstance(content, str):
+            enriched_messages.append(msg_copy)
+            continue
+
+        if not isinstance(content, list):
+            enriched_messages.append(msg_copy)
+            continue
+
+        enriched_parts: List[Any] = []
+        for part in content:
+            if not isinstance(part, dict):
+                enriched_parts.append(part)
+                continue
+
+            part_type = str(part.get("type", "")).strip().lower()
+            if part_type in {"image_url", "input_image"}:
+                img_ref = part.get("image_url")
+                if isinstance(img_ref, dict):
+                    img_url = img_ref.get("url")
+                else:
+                    img_url = img_ref
+                if isinstance(img_url, str) and img_url in url_to_desc:
+                    # Replace image part with text part
+                    enriched_parts.append({
+                        "type": "text",
+                        "text": url_to_desc[img_url],
+                    })
+                else:
+                    # Keep original if URL not found (shouldn't happen)
+                    enriched_parts.append(part)
+            else:
+                enriched_parts.append(part)
+
+        msg_copy["content"] = enriched_parts
+        enriched_messages.append(msg_copy)
+
+    # Return modified body
+    body_copy = dict(body)
+    body_copy["messages"] = enriched_messages
+    return body_copy
+
+
 def _session_chat_user_message(body: Dict[str, Any], *, param: str = "message") -> tuple[Any, Optional["web.Response"]]:
     """Parse and normalize session chat ``message`` / ``input`` like chat completions."""
     user_message = body.get("message") or body.get("input")