diff --git a/README.md b/README.md
index a22febc..798e97b 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,13 @@ Alternatively, if you only need the most likely category, you can use this:
 This will prompt for a string and classify it, outputting the category on
 stdout (or "None" if it cannot determine anything).
 
+### Checking individual words or ngrams
+
+    gptc check <compiled model file> <token or ngram>
+
+This is very similar to `gptc classify`, except it takes the input as an
+argument, and it treats the input as a single token or ngram.
+
 ### Compiling models
 
     gptc compile [-n <max_ngram_length>] [-c <min_count>] <raw model file>
diff --git a/gptc/__main__.py b/gptc/__main__.py
index 11cd795..32ec754 100644
--- a/gptc/__main__.py
+++ b/gptc/__main__.py
@@ -53,6 +53,10 @@ def main() -> None:
         action="store_true",
     )
 
+    check_parser = subparsers.add_parser("check", help="check one word or ngram in model")
+    check_parser.add_argument("model", help="compiled model to use")
+    check_parser.add_argument("token", help="token or ngram to check")
+
     pack_parser = subparsers.add_parser("pack", help="pack a model from a directory")
     pack_parser.add_argument("model", help="directory containing model")
 
@@ -81,6 +85,10 @@ def main() -> None:
         else:
             probabilities = model.confidence(text, args.max_ngram_length)
             print(json.dumps(probabilities))
+    elif args.subparser_name == "check":
+        with open(args.model, "rb") as f:
+            model = gptc.deserialize(f.read())
+        print(json.dumps(model.get(args.token)))
     else:
         print(json.dumps(gptc.pack(args.model, True)[0]))