diff --git a/sgl-router/Cargo.toml b/sgl-router/Cargo.toml
index 2460b635a..e0defacdf 100644
--- a/sgl-router/Cargo.toml
+++ b/sgl-router/Cargo.toml
@@ -6,6 +6,7 @@ edition = "2021"
 [features]
 default = ["huggingface"]
 huggingface = ["tokenizers"]
+tiktoken = ["tiktoken-rs"]
 
 [lib]
 name = "sglang_router_rs"
@@ -49,6 +50,7 @@ url = "2.5.4"
 tokio-stream = { version = "0.1", features = ["sync"] }
 anyhow = "1.0"
 tokenizers = { version = "0.21.4", optional = true }
+tiktoken-rs = { version = "0.5", optional = true }
 
 [dev-dependencies]
 criterion = { version = "0.5", features = ["html_reports"] }
diff --git a/sgl-router/src/tokenizer/factory.rs b/sgl-router/src/tokenizer/factory.rs
index 04b950d3c..e339140e7 100644
--- a/sgl-router/src/tokenizer/factory.rs
+++ b/sgl-router/src/tokenizer/factory.rs
@@ -1,4 +1,4 @@
-use super::{traits, TokenizerTrait};
+use super::traits::{self, Tokenizer as TokenizerTrait};
 use crate::metrics::TokenizerMetrics;
 use anyhow::{Error, Result};
 use std::fs::File;
@@ -15,7 +15,9 @@ use super::huggingface::HuggingFaceTokenizer;
 pub enum TokenizerType {
     HuggingFace(String),
     Mock,
-    // Future: SentencePiece, GGUF, Tiktoken
+    #[cfg(feature = "tiktoken")]
+    Tiktoken(String),
+    // Future: SentencePiece, GGUF
 }
 
 /// Create a tokenizer from a file path to a tokenizer file.
@@ -166,6 +168,23 @@ pub fn create_tokenizer(model_name_or_path: &str) -> Result<Arc<dyn traits::Toke
         return create_tokenizer_from_file(model_name_or_path);
     }
 
+    // Check if it's a GPT model name that should use Tiktoken
+    #[cfg(feature = "tiktoken")]
+    {
+        if model_name_or_path.contains("gpt-")
+            || model_name_or_path.contains("davinci")
+            || model_name_or_path.contains("curie")
+            || model_name_or_path.contains("babbage")
+            || model_name_or_path.contains("ada")
+        {
+            use super::tiktoken::TiktokenTokenizer;
+            let tokenizer = TiktokenTokenizer::from_model_name(model_name_or_path)?;
+            TokenizerMetrics::record_factory_load("tiktoken");
+            TokenizerMetrics::set_vocab_size("tiktoken", tokenizer.vocab_size());
+            return Ok(Arc::new(tokenizer));
+        }
+    }
+
     // Otherwise, try to load from HuggingFace Hub
     #[cfg(feature = "huggingface")]
     {
@@ -245,4 +264,18 @@ mod tests {
             assert!(e.to_string().contains("File not found"));
         }
     }
+
+    #[cfg(feature = "tiktoken")]
+    #[test]
+    fn test_create_tiktoken_tokenizer() {
+        // Test creating tokenizer for GPT models
+        let tokenizer = create_tokenizer("gpt-4").unwrap();
+        assert!(tokenizer.vocab_size() > 0);
+
+        // Test encoding and decoding
+        let text = "Hello, world!";
+        let encoding = tokenizer.encode(text).unwrap();
+        let decoded = tokenizer.decode(&encoding.token_ids(), false).unwrap();
+        assert_eq!(decoded, text);
+    }
 }
diff --git a/sgl-router/src/tokenizer/mod.rs b/sgl-router/src/tokenizer/mod.rs
index c218dbecc..7d7f87aed 100644
--- a/sgl-router/src/tokenizer/mod.rs
+++ b/sgl-router/src/tokenizer/mod.rs
@@ -4,6 +4,7 @@ use std::sync::Arc;
 
 pub mod factory;
 pub mod mock;
+pub mod sequence;
 pub mod stop;
 pub mod stream;
 pub mod traits;
@@ -12,11 +13,15 @@ pub mod traits;
 #[cfg(feature = "huggingface")]
 pub mod huggingface;
 
+#[cfg(feature = "tiktoken")]
+pub mod tiktoken;
+
 #[cfg(test)]
 mod tests;
 
 // Re-exports
 pub use factory::{create_tokenizer, create_tokenizer_from_file, TokenizerType};
+pub use sequence::Sequence;
 pub use stop::{SequenceDecoderOutput, StopSequenceConfig, StopSequenceDecoder};
 pub use stream::DecodeStream;
 pub use traits::{Decoder, Encoder, Encoding, SpecialTokens, Tokenizer as TokenizerTrait};
@@ -24,6 +29,9 @@ pub use traits::{Decoder, Encoder, Encoding, SpecialTokens, Tokenizer as Tokeniz
 #[cfg(feature = "huggingface")]
 pub use huggingface::{ChatMessage, HuggingFaceTokenizer};
 
+#[cfg(feature = "tiktoken")]
+pub use tiktoken::{TiktokenModel, TiktokenTokenizer};
+
 /// Main tokenizer wrapper that provides a unified interface for different tokenizer implementations
 #[derive(Clone)]
 pub struct Tokenizer(Arc<dyn traits::Tokenizer>);
diff --git a/sgl-router/src/tokenizer/sequence.rs b/sgl-router/src/tokenizer/sequence.rs
new file mode 100644
index 000000000..816d3cc59
--- /dev/null
+++ b/sgl-router/src/tokenizer/sequence.rs
@@ -0,0 +1,238 @@
+use super::traits::Tokenizer as TokenizerTrait;
+use anyhow::Result;
+use std::sync::Arc;
+
+/// Maintains state for an ongoing sequence of tokens and their decoded text
+/// This provides a cleaner abstraction for managing token sequences
+pub struct Sequence {
+    /// The tokenizer used for encoding/decoding
+    tokenizer: Arc<dyn TokenizerTrait>,
+
+    /// The current sequence of token ids
+    token_ids: Vec<u32>,
+
+    /// The position in the current sequence the last decoded token completed
+    prefix_offset: usize,
+
+    /// Current position in the sequence
+    read_offset: usize,
+}
+
+impl std::fmt::Debug for Sequence {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("Sequence")
+            .field("tokenizer", &"Arc<dyn Tokenizer>")
+            .field(
+                "token_ids",
+                &format_args!("{}", {
+                    let token_ids = self.token_ids();
+                    if token_ids.len() <= 20 {
+                        format!("{:?}", token_ids)
+                    } else {
+                        let first_ten = &token_ids[..10];
+                        let last_ten = &token_ids[token_ids.len() - 10..];
+                        format!("{:?} ... {:?}", first_ten, last_ten)
+                    }
+                }),
+            )
+            .field("prefix_offset", &self.prefix_offset)
+            .field("read_offset", &self.read_offset)
+            .field("token count", &self.token_ids.len())
+            .finish()
+    }
+}
+
+impl Sequence {
+    /// Create a new empty sequence
+    pub fn new(tokenizer: Arc<dyn TokenizerTrait>) -> Self {
+        Self {
+            tokenizer,
+            token_ids: Vec::new(),
+            prefix_offset: 0,
+            read_offset: 0,
+        }
+    }
+
+    /// Create a sequence with initial tokens
+    pub fn with_tokens(tokenizer: Arc<dyn TokenizerTrait>, token_ids: Vec<u32>) -> Self {
+        let len = token_ids.len();
+        Self {
+            tokenizer,
+            token_ids,
+            prefix_offset: 0,
+            read_offset: len,
+        }
+    }
+
+    /// Check if the sequence is empty
+    pub fn is_empty(&self) -> bool {
+        self.token_ids.is_empty()
+    }
+
+    /// Get the length of the sequence
+    pub fn len(&self) -> usize {
+        self.token_ids.len()
+    }
+
+    /// Clear the sequence
+    pub fn clear(&mut self) {
+        self.token_ids.clear();
+        self.prefix_offset = 0;
+        self.read_offset = 0;
+    }
+
+    /// Append text to the sequence by encoding it
+    pub fn append_text(&mut self, input: &str) -> Result<()> {
+        let encoding = self.tokenizer.encode(input)?;
+        self.token_ids.extend(encoding.token_ids());
+        Ok(())
+    }
+
+    /// Append a single token to the sequence and return newly decoded text
+    /// Based on HuggingFace TGI incremental decoding
+    pub fn append_token(&mut self, token_id: u32) -> Result<String> {
+        // Store the old read offset before adding the new token
+        let old_read_offset = self.read_offset;
+
+        self.token_ids.push(token_id);
+        self.read_offset = self.token_ids.len();
+
+        // If this is the first token or we're at the beginning, decode everything
+        if self.prefix_offset == 0 && old_read_offset == 0 {
+            let text = self.tokenizer.decode(&self.token_ids, false)?;
+            if text.ends_with("�") {
+                // Incomplete UTF-8 sequence, wait for more tokens
+                return Ok(String::new());
+            }
+            self.prefix_offset = 0;
+            return Ok(text);
+        }
+
+        // Decode the text up to the previous position
+        let prefix_text = self
+            .tokenizer
+            .decode(&self.token_ids[self.prefix_offset..old_read_offset], false)?;
+
+        // Decode the text including the new token
+        let new_text = self
+            .tokenizer
+            .decode(&self.token_ids[self.prefix_offset..], false)?;
+
+        // Handle multi-byte character boundaries
+        let mut prefix_text_len = prefix_text.len();
+        while !new_text.is_char_boundary(prefix_text_len) && prefix_text_len > 0 {
+            prefix_text_len -= 1;
+        }
+
+        if new_text.len() > prefix_text.len() {
+            if new_text.ends_with("�") {
+                // Incomplete UTF-8 sequence, wait for more tokens
+                return Ok(String::new());
+            } else {
+                // Return the new text portion
+                let incremental_text = new_text[prefix_text_len..].to_string().replace("�", "");
+                self.prefix_offset = old_read_offset;
+                return Ok(incremental_text);
+            }
+        }
+
+        Ok(String::new())
+    }
+
+    /// Get a reference to the tokenizer
+    pub fn tokenizer(&self) -> &Arc<dyn TokenizerTrait> {
+        &self.tokenizer
+    }
+
+    /// Get the current token ids
+    pub fn token_ids(&self) -> &[u32] {
+        &self.token_ids
+    }
+
+    /// Decode the entire sequence to text
+    pub fn text(&self) -> Result<String> {
+        self.tokenizer.decode(&self.token_ids, false)
+    }
+
+    /// Get the prefix offset
+    pub fn prefix_offset(&self) -> usize {
+        self.prefix_offset
+    }
+
+    /// Get the read offset
+    pub fn read_offset(&self) -> usize {
+        self.read_offset
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::tokenizer::mock::MockTokenizer;
+
+    #[test]
+    fn test_sequence_new() {
+        let tokenizer = Arc::new(MockTokenizer::new());
+        let seq = Sequence::new(tokenizer);
+        assert!(seq.is_empty());
+        assert_eq!(seq.len(), 0);
+    }
+
+    #[test]
+    fn test_sequence_append_text() {
+        let tokenizer = Arc::new(MockTokenizer::new());
+        let mut seq = Sequence::new(tokenizer);
+
+        seq.append_text("Hello").unwrap();
+        assert!(!seq.is_empty());
+        assert!(!seq.is_empty());
+
+        let text = seq.text().unwrap();
+        assert_eq!(text, "Hello");
+    }
+
+    #[test]
+    fn test_sequence_append_token() {
+        let tokenizer = Arc::new(MockTokenizer::new());
+        let mut seq = Sequence::new(tokenizer.clone());
+
+        // Start with an empty sequence and append token 1 ("Hello")
+        let text1 = seq.append_token(1).unwrap();
+        assert_eq!(text1, "Hello");
+
+        // Now append token 2 ("world")
+        // The mock tokenizer will decode [1, 2] as "Hello world" (with a space)
+        let text2 = seq.append_token(2).unwrap();
+        // The incremental text should be " world" (with the space that the mock tokenizer adds)
+        assert_eq!(text2, " world");
+
+        // Verify the full text
+        assert_eq!(seq.text().unwrap(), "Hello world");
+    }
+
+    #[test]
+    fn test_sequence_clear() {
+        let tokenizer = Arc::new(MockTokenizer::new());
+        let mut seq = Sequence::new(tokenizer);
+
+        seq.append_text("Hello world").unwrap();
+        assert!(!seq.is_empty());
+
+        seq.clear();
+        assert!(seq.is_empty());
+        assert_eq!(seq.len(), 0);
+        assert_eq!(seq.prefix_offset(), 0);
+        assert_eq!(seq.read_offset(), 0);
+    }
+
+    #[test]
+    fn test_sequence_debug() {
+        let tokenizer = Arc::new(MockTokenizer::new());
+        let mut seq = Sequence::new(tokenizer);
+
+        seq.append_text("Test").unwrap();
+        let debug_str = format!("{:?}", seq);
+        assert!(debug_str.contains("Sequence"));
+        assert!(debug_str.contains("token count"));
+    }
+}
diff --git a/sgl-router/src/tokenizer/tests.rs b/sgl-router/src/tokenizer/tests.rs
index 2c4d4b108..93c8f1621 100644
--- a/sgl-router/src/tokenizer/tests.rs
+++ b/sgl-router/src/tokenizer/tests.rs
@@ -129,7 +129,9 @@ fn test_thread_safety() {
             thread::spawn(move || {
                 let text = "Hello test".to_string();
                 let encoding = tokenizer_clone.encode(&text).unwrap();
-                let decoded = tokenizer_clone.decode(encoding.token_ids(), false).unwrap();
+                let decoded = tokenizer_clone
+                    .decode(&encoding.token_ids(), false)
+                    .unwrap();
                 assert!(decoded.contains("Hello") || decoded.contains("test"));
                 i
             })
diff --git a/sgl-router/src/tokenizer/tiktoken.rs b/sgl-router/src/tokenizer/tiktoken.rs
new file mode 100644
index 000000000..4cf0ea9f1
--- /dev/null
+++ b/sgl-router/src/tokenizer/tiktoken.rs
@@ -0,0 +1,276 @@
+use super::traits::{Decoder, Encoder, Encoding, SpecialTokens, Tokenizer as TokenizerTrait};
+use anyhow::{Error, Result};
+use tiktoken_rs::{cl100k_base, p50k_base, p50k_edit, r50k_base, CoreBPE};
+
+/// Tiktoken tokenizer wrapper for OpenAI GPT models
+pub struct TiktokenTokenizer {
+    tokenizer: CoreBPE,
+    #[allow(dead_code)]
+    model: TiktokenModel,
+    special_tokens: SpecialTokens,
+    vocab_size: usize,
+}
+
+/// Supported Tiktoken models
+#[derive(Debug, Clone, Copy)]
+pub enum TiktokenModel {
+    /// GPT-4, GPT-3.5-turbo, text-embedding-ada-002
+    Cl100kBase,
+    /// Codex models, text-davinci-002, text-davinci-003
+    P50kBase,
+    /// Use for edit models like text-davinci-edit-001, code-davinci-edit-001
+    P50kEdit,
+    /// GPT-3 models like davinci
+    R50kBase,
+}
+
+impl TiktokenTokenizer {
+    /// Create a new Tiktoken tokenizer for the specified model
+    pub fn new(model: TiktokenModel) -> Result<Self> {
+        let tokenizer =
+            match model {
+                TiktokenModel::Cl100kBase => cl100k_base()
+                    .map_err(|e| Error::msg(format!("Failed to load cl100k_base: {}", e)))?,
+                TiktokenModel::P50kBase => p50k_base()
+                    .map_err(|e| Error::msg(format!("Failed to load p50k_base: {}", e)))?,
+                TiktokenModel::P50kEdit => p50k_edit()
+                    .map_err(|e| Error::msg(format!("Failed to load p50k_edit: {}", e)))?,
+                TiktokenModel::R50kBase => r50k_base()
+                    .map_err(|e| Error::msg(format!("Failed to load r50k_base: {}", e)))?,
+            };
+
+        // Extract special tokens (tiktoken-rs doesn't expose them directly)
+        // We'll use common ones for GPT models
+        let special_tokens = Self::get_special_tokens_for_model(model);
+
+        // Get vocabulary size (this is an approximation)
+        let vocab_size = match model {
+            TiktokenModel::Cl100kBase => 100256, // cl100k has ~100k tokens
+            TiktokenModel::P50kBase | TiktokenModel::P50kEdit => 50281, // p50k has ~50k tokens
+            TiktokenModel::R50kBase => 50257,    // r50k has ~50k tokens
+        };
+
+        Ok(TiktokenTokenizer {
+            tokenizer,
+            model,
+            special_tokens,
+            vocab_size,
+        })
+    }
+
+    /// Create a tokenizer from a model string (e.g., "gpt-4", "gpt-3.5-turbo")
+    pub fn from_model_name(model_name: &str) -> Result<Self> {
+        let model = Self::model_from_name(model_name)?;
+        Self::new(model)
+    }
+
+    /// Determine the appropriate model from a model name
+    fn model_from_name(model_name: &str) -> Result<TiktokenModel> {
+        // Based on OpenAI's model-to-encoding mapping
+        if model_name.contains("gpt-4")
+            || model_name.contains("gpt-3.5")
+            || model_name.contains("turbo")
+        {
+            Ok(TiktokenModel::Cl100kBase)
+        } else if model_name.contains("davinci-002")
+            || model_name.contains("davinci-003")
+            || model_name.contains("codex")
+        {
+            Ok(TiktokenModel::P50kBase)
+        } else if model_name.contains("edit") {
+            Ok(TiktokenModel::P50kEdit)
+        } else if model_name.contains("davinci")
+            || model_name.contains("curie")
+            || model_name.contains("babbage")
+            || model_name.contains("ada")
+        {
+            Ok(TiktokenModel::R50kBase)
+        } else {
+            // Return an error for unrecognized model names to prevent silent failures
+            Err(anyhow::anyhow!(
+                "Unrecognized OpenAI model name: '{}'. Expected GPT-3, GPT-3.5, GPT-4, or related model names",
+                model_name
+            ))
+        }
+    }
+
+    /// Get special tokens for a specific model
+    fn get_special_tokens_for_model(model: TiktokenModel) -> SpecialTokens {
+        // These are common special tokens for GPT models
+        // The actual token IDs might vary by model
+        match model {
+            TiktokenModel::Cl100kBase => SpecialTokens {
+                bos_token: Some("<|endoftext|>".to_string()),
+                eos_token: Some("<|endoftext|>".to_string()),
+                unk_token: None,
+                sep_token: None,
+                pad_token: Some("<|endoftext|>".to_string()),
+                cls_token: None,
+                mask_token: None,
+                additional_special_tokens: vec![
+                    "<|fim_prefix|>".to_string(),
+                    "<|fim_middle|>".to_string(),
+                    "<|fim_suffix|>".to_string(),
+                    "<|endofprompt|>".to_string(),
+                ],
+            },
+            _ => SpecialTokens {
+                bos_token: Some("<|endoftext|>".to_string()),
+                eos_token: Some("<|endoftext|>".to_string()),
+                unk_token: None,
+                sep_token: None,
+                pad_token: Some("<|endoftext|>".to_string()),
+                cls_token: None,
+                mask_token: None,
+                additional_special_tokens: vec![],
+            },
+        }
+    }
+}
+
+impl Encoder for TiktokenTokenizer {
+    fn encode(&self, input: &str) -> Result<Encoding> {
+        let tokens = self.tokenizer.encode_ordinary(input);
+        Ok(Encoding::Tiktoken(tokens))
+    }
+
+    fn encode_batch(&self, inputs: &[&str]) -> Result<Vec<Encoding>> {
+        inputs.iter().map(|input| self.encode(input)).collect()
+    }
+}
+
+impl Decoder for TiktokenTokenizer {
+    fn decode(&self, token_ids: &[u32], _skip_special_tokens: bool) -> Result<String> {
+        // Convert u32 to usize for tiktoken-rs
+        let tokens: Vec<usize> = token_ids.iter().map(|&id| id as usize).collect();
+
+        self.tokenizer
+            .decode(tokens)
+            .map_err(|e| Error::msg(format!("Decoding failed: {}", e)))
+    }
+}
+
+impl TokenizerTrait for TiktokenTokenizer {
+    fn vocab_size(&self) -> usize {
+        self.vocab_size
+    }
+
+    fn get_special_tokens(&self) -> &SpecialTokens {
+        &self.special_tokens
+    }
+
+    fn token_to_id(&self, _token: &str) -> Option<u32> {
+        // Tiktoken doesn't provide direct token-to-id mapping
+        // We'd need to encode the token and check if it produces a single ID
+        None
+    }
+
+    fn id_to_token(&self, _id: u32) -> Option<String> {
+        // Tiktoken doesn't provide direct id-to-token mapping
+        // We can only decode IDs to text
+        None
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_tiktoken_creation() {
+        let tokenizer = TiktokenTokenizer::new(TiktokenModel::Cl100kBase).unwrap();
+        assert_eq!(tokenizer.vocab_size(), 100256);
+    }
+
+    #[test]
+    fn test_model_from_name() {
+        assert!(matches!(
+            TiktokenTokenizer::model_from_name("gpt-4").unwrap(),
+            TiktokenModel::Cl100kBase
+        ));
+        assert!(matches!(
+            TiktokenTokenizer::model_from_name("gpt-3.5-turbo").unwrap(),
+            TiktokenModel::Cl100kBase
+        ));
+        assert!(matches!(
+            TiktokenTokenizer::model_from_name("text-davinci-003").unwrap(),
+            TiktokenModel::P50kBase
+        ));
+        assert!(matches!(
+            TiktokenTokenizer::model_from_name("text-davinci-edit-001").unwrap(),
+            TiktokenModel::P50kEdit
+        ));
+        assert!(matches!(
+            TiktokenTokenizer::model_from_name("davinci").unwrap(),
+            TiktokenModel::R50kBase
+        ));
+    }
+
+    #[test]
+    fn test_encode_decode() {
+        let tokenizer = TiktokenTokenizer::new(TiktokenModel::Cl100kBase).unwrap();
+
+        let text = "Hello, world!";
+        let encoding = tokenizer.encode(text).unwrap();
+
+        let decoded = tokenizer.decode(&encoding.token_ids(), false).unwrap();
+        assert_eq!(decoded, text);
+    }
+
+    #[test]
+    fn test_batch_encode() {
+        let tokenizer = TiktokenTokenizer::new(TiktokenModel::Cl100kBase).unwrap();
+
+        let texts = vec!["Hello", "World", "Test"];
+        let encodings = tokenizer.encode_batch(&texts).unwrap();
+
+        assert_eq!(encodings.len(), 3);
+        for (i, encoding) in encodings.iter().enumerate() {
+            let decoded = tokenizer.decode(&encoding.token_ids(), false).unwrap();
+            assert_eq!(decoded, texts[i]);
+        }
+    }
+
+    #[test]
+    fn test_special_tokens() {
+        let tokenizer = TiktokenTokenizer::new(TiktokenModel::Cl100kBase).unwrap();
+        let special_tokens = tokenizer.get_special_tokens();
+
+        assert!(special_tokens.eos_token.is_some());
+        assert_eq!(special_tokens.eos_token.as_ref().unwrap(), "<|endoftext|>");
+    }
+
+    #[test]
+    fn test_unrecognized_model_name_returns_error() {
+        // Test that unrecognized model names return an error
+        let result = TiktokenTokenizer::from_model_name("distilgpt-2");
+        assert!(result.is_err());
+        if let Err(e) = result {
+            assert!(e.to_string().contains("Unrecognized OpenAI model name"));
+        }
+
+        let result = TiktokenTokenizer::from_model_name("bert-base-uncased");
+        assert!(result.is_err());
+        if let Err(e) = result {
+            assert!(e.to_string().contains("Unrecognized OpenAI model name"));
+        }
+
+        let result = TiktokenTokenizer::from_model_name("llama-7b");
+        assert!(result.is_err());
+        if let Err(e) = result {
+            assert!(e.to_string().contains("Unrecognized OpenAI model name"));
+        }
+    }
+
+    #[test]
+    fn test_recognized_model_names() {
+        // Test that recognized model names work correctly
+        assert!(TiktokenTokenizer::from_model_name("gpt-4").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("gpt-3.5-turbo").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("text-davinci-003").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("code-davinci-002").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("text-curie-001").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("text-babbage-001").is_ok());
+        assert!(TiktokenTokenizer::from_model_name("text-ada-001").is_ok());
+    }
+}
diff --git a/sgl-router/src/tokenizer/traits.rs b/sgl-router/src/tokenizer/traits.rs
index 54e683497..e0153704a 100644
--- a/sgl-router/src/tokenizer/traits.rs
+++ b/sgl-router/src/tokenizer/traits.rs
@@ -26,13 +26,28 @@ pub enum Encoding {
     Hf(Box<tokenizers::tokenizer::Encoding>),
     /// Sentence Piece
     Sp(Vec<u32>),
+    /// Tiktoken (for GPT models)
+    Tiktoken(Vec<usize>),
 }
 
 impl Encoding {
-    pub fn token_ids(&self) -> &[u32] {
+    pub fn token_ids(&self) -> Vec<u32> {
+        match self {
+            Encoding::Hf(inner) => inner.get_ids().to_vec(),
+            Encoding::Sp(inner) => inner.clone(),
+            Encoding::Tiktoken(inner) => inner.iter().map(|&id| id as u32).collect(),
+        }
+    }
+
+    pub fn token_ids_ref(&self) -> &[u32] {
         match self {
             Encoding::Hf(inner) => inner.get_ids(),
             Encoding::Sp(inner) => inner,
+            Encoding::Tiktoken(_) => {
+                // Tiktoken uses usize, we can't return a reference to u32
+                // This is a limitation - callers should use token_ids() for Tiktoken
+                &[]
+            }
         }
     }
 }