diff --git a/package-lock.json b/package-lock.json
index aefbf0c..269d7a2 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -9,6 +9,7 @@
       "version": "1.0.0",
       "license": "MIT",
       "dependencies": {
+        "@anthropic-ai/tokenizer": "^0.0.4",
         "axios": "^1.3.5",
         "cookie-parser": "^1.4.6",
         "copyfiles": "^2.4.1",
@@ -47,6 +48,15 @@
         "node": ">=18.0.0"
       }
     },
+    "node_modules/@anthropic-ai/tokenizer": {
+      "version": "0.0.4",
+      "resolved": "https://registry.npmjs.org/@anthropic-ai/tokenizer/-/tokenizer-0.0.4.tgz",
+      "integrity": "sha512-EHRKbxlxlc8W4KCBEseByJ7YwyYCmgu9OyN59H9+IYIGPoKv8tXyQXinkeGDI+cI8Tiuz9wk2jZb/kK7AyvL7g==",
+      "dependencies": {
+        "@types/node": "^18.11.18",
+        "tiktoken": "^1.0.10"
+      }
+    },
     "node_modules/@babel/parser": {
       "version": "7.22.7",
       "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.22.7.tgz",
diff --git a/package.json b/package.json
index 8edee78..5b5136b 100644
--- a/package.json
+++ b/package.json
@@ -16,6 +16,7 @@
   "author": "",
   "license": "MIT",
   "dependencies": {
+    "@anthropic-ai/tokenizer": "^0.0.4",
     "axios": "^1.3.5",
     "cookie-parser": "^1.4.6",
     "copyfiles": "^2.4.1",
diff --git a/src/proxy/middleware/request/check-context-size.ts b/src/proxy/middleware/request/check-context-size.ts
index 16e5db6..bfdc2fa 100644
--- a/src/proxy/middleware/request/check-context-size.ts
+++ b/src/proxy/middleware/request/check-context-size.ts
@@ -7,14 +7,6 @@ import { RequestPreprocessor } from ".";
 const CLAUDE_MAX_CONTEXT = config.maxContextTokensAnthropic;
 const OPENAI_MAX_CONTEXT = config.maxContextTokensOpenAI;
 
-/**
- * Claude models don't throw an error if you exceed the token limit and
- * instead just become extremely slow and provide schizo output. To be safe,
- * we will only allow 95% of the stated limit, which also accounts for our
- * tokenization being slightly different than Anthropic's.
- */
-const CLAUDE_TOKEN_LIMIT_ADJUSTMENT = 0.95;
-
 /**
  * Assigns `req.promptTokens` and `req.outputTokens` based on the request body
  * and outbound API format, which combined determine the size of the context.
@@ -71,11 +63,11 @@ function validateContextSize(req: Request) {
   } else if (model.match(/gpt-4/)) {
     modelMax = 8192;
   } else if (model.match(/claude-(?:instant-)?v1(?:\.\d)?(?:-100k)/)) {
-    modelMax = 100000 * CLAUDE_TOKEN_LIMIT_ADJUSTMENT;
+    modelMax = 100000;
   } else if (model.match(/claude-(?:instant-)?v1(?:\.\d)?$/)) {
-    modelMax = 9000 * CLAUDE_TOKEN_LIMIT_ADJUSTMENT;
+    modelMax = 9000;
   } else if (model.match(/claude-2/)) {
-    modelMax = 100000 * CLAUDE_TOKEN_LIMIT_ADJUSTMENT;
+    modelMax = 100000;
   } else {
     // Don't really want to throw here because I don't want to have to update
     // this ASAP every time a new model is released.
diff --git a/src/tokenization/claude.ts b/src/tokenization/claude.ts
index b37eab6..1eeac93 100644
--- a/src/tokenization/claude.ts
+++ b/src/tokenization/claude.ts
@@ -1,21 +1,6 @@
-// For now this is just using the GPT vocabulary, even though Claude has a
-// different one. Token counts won't be perfect so this just provides
-// a rough estimate.
-//
-// TODO: use huggingface tokenizers instead of openai's tiktoken library since
-// that should support the vocabulary file Anthropic provides.
-
-import { Tiktoken } from "tiktoken/lite";
-import cl100k_base from "tiktoken/encoders/cl100k_base.json";
-
-let encoder: Tiktoken;
+import { countTokens } from "@anthropic-ai/tokenizer";
 
 export function init() {
-  encoder = new Tiktoken(
-    cl100k_base.bpe_ranks,
-    cl100k_base.special_tokens,
-    cl100k_base.pat_str
-  );
   return true;
 }
 
@@ -24,13 +9,13 @@ export function getTokenCount(prompt: string, _model: string) {
   // 500k characters should be sufficient for all supported models.
   if (prompt.length > 500000) {
     return {
-      tokenizer: "tiktoken (prompt length limit exceeded)",
+      tokenizer: "length fallback",
       token_count: 100000,
     };
   }
 
   return {
-    tokenizer: "tiktoken (cl100k_base)",
-    token_count: encoder.encode(prompt).length,
+    tokenizer: "@anthropic-ai/tokenizer",
+    token_count: countTokens(prompt),
   };
 }