diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index 237b1ca52..478b1034a 100755
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -196,7 +196,7 @@ def show_stats(dirnames):
         arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
     )
     ax.annotate(
-        "Second attempt,\nincluding\nunit test errors",
+        "Second attempt,\nincluding unit\ntest error output",
         xy=(2.55, 56),
         xytext=(3.5, top),
         horizontalalignment="center",
diff --git a/docs/benchmarks-1106.md b/docs/benchmarks-1106.md
index ecf00ee22..2e15972de 100644
--- a/docs/benchmarks-1106.md
+++ b/docs/benchmarks-1106.md
@@ -35,13 +35,15 @@ With that in mind, I've been benchmarking the new models.
 ## gpt-4-1106-preview
 
 - The new `gpt-4-1106-preview` model seems **much faster** than the earlier GPT-4 models! I won't be able to properly quantify this until the rate limits loosen up. Currently I am seeing 10X faster responses.
-- **It is better at producing correct code on the first try**. It gets ~59% of the coding exercises correct, without needing to see errors from the test suite. Previous models only get 46-47% of the exercises correct on the first try.
-- The new model seems to perform similarly to the old models after being given a chance to correct bugs by reviewing test suite error output.
+- **It is better at producing correct code on the first try**. It gets ~60% of the coding exercises correct, without needing to see errors from the test suite. Previous models only get 46-47% of the exercises correct on the first try.
+- The new model seems to perform somewhat better (69%) than the old models (63-64%) after being given a chance to correct bugs by reviewing test suite error output.
 
 **These results are preliminiary.**
 OpenAI is enforcing very low
 rate limits on the new GPT-4 model. The limits are so low, that
-I have only been able to attempt 56 out of 133 exercism problems.
+I have only been able to attempt
+58
+out of 133 exercism problems.
 They are randomly chosen, so results should be *roughly*
 indicative of the full benchmark.