diff --git a/headroom/tokenizers/tiktoken_counter.py b/headroom/tokenizers/tiktoken_counter.py
index 5c05f308d..87a4ef0fa 100644
--- a/headroom/tokenizers/tiktoken_counter.py
+++ b/headroom/tokenizers/tiktoken_counter.py
@@ -164,6 +164,14 @@ def get_encoding_for_model(model: str) -> str:
     Returns:
         Encoding name (e.g., 'o200k_base', 'cl100k_base').
     """
+    # Case-insensitive: TokenizerRegistry lowercases only its *cache key*, then
+    # constructs the counter from the caller's original string. An uppercase
+    # deployment name ("GPT-4o", routine on Azure) therefore arrived here
+    # verbatim, matched no prefix, and silently took DEFAULT_ENCODING -- so the
+    # encoding a model got depended on the casing of whichever request warmed
+    # the cache first, and flipped across restarts.
+    model = model.lower()
+
     # Direct lookup
     if model in MODEL_TO_ENCODING:
         return MODEL_TO_ENCODING[model]
@@ -180,6 +188,9 @@ def get_encoding_for_model(model: str) -> str:
         # which they would otherwise match and be mis-encoded as cl100k_base.
         ("gpt-4.1", "o200k_base"),
         ("gpt-4.5", "o200k_base"),
+        # gpt-5 uses o200k_base. Without this it fell through to the
+        # cl100k_base default, over-counting CJK by ~33%.
+        ("gpt-5", "o200k_base"),
         ("gpt-4-turbo", "cl100k_base"),
         ("gpt-4", "cl100k_base"),
         ("gpt-3.5", "cl100k_base"),
diff --git a/tests/test_tokenizer_encoding_resolution.py b/tests/test_tokenizer_encoding_resolution.py
new file mode 100644
index 000000000..3dd1b6be8
--- /dev/null
+++ b/tests/test_tokenizer_encoding_resolution.py
@@ -0,0 +1,84 @@
+"""``get_encoding_for_model`` must not depend on casing, and must know gpt-5.
+
+Two defects, both reachable through the normal ``get_tokenizer()`` path:
+
+1. **gpt-5 had no prefix entry**, so it fell through to ``DEFAULT_ENCODING``
+   (``cl100k_base``) instead of ``o200k_base``. On CJK text cl100k emits ~33%
+   more tokens than o200k, so every gpt-5 count was inflated.
+
+2. **Resolution was case-sensitive.** ``TokenizerRegistry.get`` lowercases only
+   its *cache key*, then builds the counter from the caller's original string
+   (``_create_tokenizer(model, ...)``). An uppercase deployment name -- routine
+   on Azure, where the deployment name is user-chosen -- reached the resolver
+   verbatim, matched nothing, and took the default encoding.
+
+   The cache made (2) genuinely nasty: because the key is lowercased but
+   construction is not, the encoding a model ends up with depended on the
+   casing of whichever request warmed the cache first, and could differ across
+   restarts. The tests below call ``clear_cache()`` so the uppercase spelling is
+   resolved cold, which is the failing order.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from headroom.tokenizers import get_tokenizer
+from headroom.tokenizers.registry import TokenizerRegistry
+from headroom.tokenizers.tiktoken_counter import get_encoding_for_model
+
+CJK = "这是一个测试文档，用于验证分词器的差异。" * 30
+
+
+@pytest.mark.parametrize(
+    ("model", "expected"),
+    [
+        # gpt-5 family: the missing entry.
+        ("gpt-5", "o200k_base"),
+        ("gpt-5-mini", "o200k_base"),
+        ("gpt-5-nano", "o200k_base"),
+        ("gpt-5-2025-08-07", "o200k_base"),
+        # Casing must not change the answer.
+        ("GPT-4o", "o200k_base"),
+        ("GPT-4.1", "o200k_base"),
+        ("Gpt-4O-Mini", "o200k_base"),
+        ("GPT-5", "o200k_base"),
+        ("O4-Mini", "o200k_base"),
+        ("GPT-4", "cl100k_base"),
+        ("GPT-4-Turbo", "cl100k_base"),
+        # Must not regress.
+        ("gpt-4o", "o200k_base"),
+        ("gpt-4.1", "o200k_base"),
+        ("gpt-4", "cl100k_base"),
+        ("gpt-4-turbo", "cl100k_base"),
+        ("gpt-3.5-turbo", "cl100k_base"),
+        ("o4-mini", "o200k_base"),
+    ],
+)
+def test_encoding_resolution(model: str, expected: str) -> None:
+    assert get_encoding_for_model(model) == expected
+
+
+@pytest.mark.parametrize("model", ["gpt-5", "GPT-4o", "GPT-4.1"])
+def test_cold_cache_uppercase_still_gets_the_right_encoding(model: str) -> None:
+    """End-to-end through the registry, with the uppercase spelling resolved first.
+
+    Without clear_cache() a preceding lowercase lookup would populate the shared
+    (lowercased) cache key and mask the defect entirely.
+    """
+    tiktoken = pytest.importorskip("tiktoken")
+    o200k = len(tiktoken.get_encoding("o200k_base").encode(CJK))
+
+    TokenizerRegistry.clear_cache()
+    assert get_tokenizer(model).count_text(CJK) == o200k
+
+
+def test_casing_is_not_load_order_dependent() -> None:
+    """The same model must count identically whichever spelling arrives first."""
+    TokenizerRegistry.clear_cache()
+    upper_first = get_tokenizer("GPT-4o").count_text(CJK)
+
+    TokenizerRegistry.clear_cache()
+    lower_first = get_tokenizer("gpt-4o").count_text(CJK)
+
+    assert upper_first == lower_first