🤖 feat: Claude Opus 4.6 - 1M Context, Premium Pricing, Adaptive Thinking (#11670)

* feat: Implement new features for Claude Opus 4.6 model

- Added support for tiered pricing based on input token count for the Claude Opus 4.6 model.
- Updated token value calculations to include inputTokenCount for accurate pricing.
- Enhanced transaction handling to apply premium rates when input tokens exceed defined thresholds.
- Introduced comprehensive tests to validate pricing logic for both standard and premium rates across various scenarios.
- Updated related utility functions and models to accommodate new pricing structure.

This change improves the flexibility and accuracy of token pricing for the Claude Opus 4.6 model, ensuring users are charged appropriately based on their usage.

* feat: Add effort field to conversation and preset schemas

- Introduced a new optional `effort` field of type `String` in both the `IPreset` and `IConversation` interfaces.
- Updated the `conversationPreset` schema to include the `effort` field, enhancing the data structure for better context management.

* chore: Clean up unused variable and comments in initialize function

* chore: update dependencies and SDK versions

- Updated @anthropic-ai/sdk to version 0.73.0 in package.json and overrides.
- Updated @anthropic-ai/vertex-sdk to version 0.14.3 in packages/api/package.json.
- Updated @librechat/agents to version 3.1.34 in packages/api/package.json.
- Refactored imports in packages/api/src/endpoints/anthropic/vertex.ts for consistency.

* chore: remove postcss-loader from dependencies

* feat: Bedrock model support for adaptive thinking configuration

- Updated .env.example to include new Bedrock model IDs for Claude Opus 4.6.
- Refactored bedrockInputParser to support adaptive thinking for Opus models, allowing for dynamic thinking configurations.
- Introduced a new function to check model compatibility with adaptive thinking.
- Added an optional `effort` field to the input schemas and updated related configurations.
- Enhanced tests to validate the new adaptive thinking logic and model configurations.

* feat: Add tests for Opus 4.6 adaptive thinking configuration

* feat: Update model references for Opus 4.6 by removing version suffix

* feat: Update @librechat/agents to version 3.1.35 in package.json and package-lock.json

* chore: @librechat/agents to version 3.1.36 in package.json and package-lock.json

* feat: Normalize inputTokenCount for spendTokens and enhance transaction handling

- Introduced normalization for promptTokens to ensure inputTokenCount does not go negative.
- Updated transaction logic to reflect normalized inputTokenCount in pricing calculations.
- Added comprehensive tests to validate the new normalization logic and its impact on transaction rates for both standard and premium models.
- Refactored related functions to improve clarity and maintainability of token value calculations.

* chore: Simplify adaptive thinking configuration in helpers.ts

- Removed unnecessary type casting for the thinking property in updatedOptions.
- Ensured that adaptive thinking is directly assigned when conditions are met, improving code clarity.

* refactor: Replace hard-coded token values with dynamic retrieval from maxTokensMap in model tests

* fix: Ensure non-negative token values in spendTokens calculations

- Updated token value retrieval to use Math.max for prompt and completion tokens, preventing negative values.
- Enhanced clarity in token calculations for both prompt and completion transactions.

* test: Add test for normalization of negative structured token values in spendStructuredTokens

- Implemented a test to ensure that negative structured token values are normalized to zero during token spending.
- Verified that the transaction rates remain consistent with the expected standard values after normalization.

* refactor: Bedrock model support for adaptive thinking and context handling

- Added tests for various alternate naming conventions of Claude models to validate adaptive thinking and context support.
- Refactored `supportsAdaptiveThinking` and `supportsContext1m` functions to utilize new parsing methods for model version extraction.
- Updated `bedrockInputParser` to handle effort configurations more effectively and strip unnecessary fields for non-adaptive models.
- Improved handling of anthropic model configurations in the input parser.

* fix: Improve token value retrieval in getMultiplier function

- Updated the token value retrieval logic to use optional chaining for better safety against undefined values.
- Added a test case to ensure that the function returns the default rate when the provided valueKey does not exist in tokenValues.

api/utils/tokens.spec.js

@@ -1,3 +1,4 @@
+/** Note: No hard-coded values should be used in this file. */
 const { EModelEndpoint } = require('librechat-data-provider');
 const {
   maxTokensMap,
@@ -626,41 +627,45 @@ describe('matchModelName', () => {
 describe('Meta Models Tests', () => {
   describe('getModelMaxTokens', () => {
     test('should return correct tokens for LLaMa 2 models', () => {
-      expect(getModelMaxTokens('llama2')).toBe(4000);
-      expect(getModelMaxTokens('llama2.70b')).toBe(4000);
-      expect(getModelMaxTokens('llama2-13b')).toBe(4000);
-      expect(getModelMaxTokens('llama2-70b')).toBe(4000);
+      const llama2Tokens = maxTokensMap[EModelEndpoint.openAI]['llama2'];
+      expect(getModelMaxTokens('llama2')).toBe(llama2Tokens);
+      expect(getModelMaxTokens('llama2.70b')).toBe(llama2Tokens);
+      expect(getModelMaxTokens('llama2-13b')).toBe(llama2Tokens);
+      expect(getModelMaxTokens('llama2-70b')).toBe(llama2Tokens);
     });
 
     test('should return correct tokens for LLaMa 3 models', () => {
-      expect(getModelMaxTokens('llama3')).toBe(8000);
-      expect(getModelMaxTokens('llama3.8b')).toBe(8000);
-      expect(getModelMaxTokens('llama3.70b')).toBe(8000);
-      expect(getModelMaxTokens('llama3-8b')).toBe(8000);
-      expect(getModelMaxTokens('llama3-70b')).toBe(8000);
+      const llama3Tokens = maxTokensMap[EModelEndpoint.openAI]['llama3'];
+      expect(getModelMaxTokens('llama3')).toBe(llama3Tokens);
+      expect(getModelMaxTokens('llama3.8b')).toBe(llama3Tokens);
+      expect(getModelMaxTokens('llama3.70b')).toBe(llama3Tokens);
+      expect(getModelMaxTokens('llama3-8b')).toBe(llama3Tokens);
+      expect(getModelMaxTokens('llama3-70b')).toBe(llama3Tokens);
     });
 
     test('should return correct tokens for LLaMa 3.1 models', () => {
-      expect(getModelMaxTokens('llama3.1:8b')).toBe(127500);
-      expect(getModelMaxTokens('llama3.1:70b')).toBe(127500);
-      expect(getModelMaxTokens('llama3.1:405b')).toBe(127500);
-      expect(getModelMaxTokens('llama3-1-8b')).toBe(127500);
-      expect(getModelMaxTokens('llama3-1-70b')).toBe(127500);
-      expect(getModelMaxTokens('llama3-1-405b')).toBe(127500);
+      const llama31Tokens = maxTokensMap[EModelEndpoint.openAI]['llama3.1:8b'];
+      expect(getModelMaxTokens('llama3.1:8b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('llama3.1:70b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('llama3.1:405b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('llama3-1-8b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('llama3-1-70b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('llama3-1-405b')).toBe(llama31Tokens);
     });
 
     test('should handle partial matches for Meta models', () => {
-      // Test with full model names
-      expect(getModelMaxTokens('meta/llama3.1:405b')).toBe(127500);
-      expect(getModelMaxTokens('meta/llama3.1:70b')).toBe(127500);
-      expect(getModelMaxTokens('meta/llama3.1:8b')).toBe(127500);
-      expect(getModelMaxTokens('meta/llama3-1-8b')).toBe(127500);
+      const llama31Tokens = maxTokensMap[EModelEndpoint.openAI]['llama3.1:8b'];
+      const llama3Tokens = maxTokensMap[EModelEndpoint.openAI]['llama3'];
+      const llama2Tokens = maxTokensMap[EModelEndpoint.openAI]['llama2'];
+      expect(getModelMaxTokens('meta/llama3.1:405b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('meta/llama3.1:70b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('meta/llama3.1:8b')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('meta/llama3-1-8b')).toBe(llama31Tokens);
 
-      // Test base versions
-      expect(getModelMaxTokens('meta/llama3.1')).toBe(127500);
-      expect(getModelMaxTokens('meta/llama3-1')).toBe(127500);
-      expect(getModelMaxTokens('meta/llama3')).toBe(8000);
-      expect(getModelMaxTokens('meta/llama2')).toBe(4000);
+      expect(getModelMaxTokens('meta/llama3.1')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('meta/llama3-1')).toBe(llama31Tokens);
+      expect(getModelMaxTokens('meta/llama3')).toBe(llama3Tokens);
+      expect(getModelMaxTokens('meta/llama2')).toBe(llama2Tokens);
     });
 
     test('should match Deepseek model variations', () => {
@@ -678,18 +683,33 @@ describe('Meta Models Tests', () => {
       );
     });
 
-    test('should return 128000 context tokens for all DeepSeek models', () => {
-      expect(getModelMaxTokens('deepseek-chat')).toBe(128000);
-      expect(getModelMaxTokens('deepseek-reasoner')).toBe(128000);
-      expect(getModelMaxTokens('deepseek-r1')).toBe(128000);
-      expect(getModelMaxTokens('deepseek-v3')).toBe(128000);
-      expect(getModelMaxTokens('deepseek.r1')).toBe(128000);
+    test('should return correct context tokens for all DeepSeek models', () => {
+      const deepseekChatTokens = maxTokensMap[EModelEndpoint.openAI]['deepseek-chat'];
+      expect(getModelMaxTokens('deepseek-chat')).toBe(deepseekChatTokens);
+      expect(getModelMaxTokens('deepseek-reasoner')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek-reasoner'],
+      );
+      expect(getModelMaxTokens('deepseek-r1')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek-r1'],
+      );
+      expect(getModelMaxTokens('deepseek-v3')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek'],
+      );
+      expect(getModelMaxTokens('deepseek.r1')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek.r1'],
+      );
     });
 
     test('should handle DeepSeek models with provider prefixes', () => {
-      expect(getModelMaxTokens('deepseek/deepseek-chat')).toBe(128000);
-      expect(getModelMaxTokens('openrouter/deepseek-reasoner')).toBe(128000);
-      expect(getModelMaxTokens('openai/deepseek-v3')).toBe(128000);
+      expect(getModelMaxTokens('deepseek/deepseek-chat')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek-chat'],
+      );
+      expect(getModelMaxTokens('openrouter/deepseek-reasoner')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek-reasoner'],
+      );
+      expect(getModelMaxTokens('openai/deepseek-v3')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['deepseek'],
+      );
     });
   });
 
@@ -728,30 +748,38 @@ describe('Meta Models Tests', () => {
     const { getModelMaxOutputTokens } = require('@librechat/api');
 
     test('should return correct max output tokens for deepseek-chat', () => {
-      expect(getModelMaxOutputTokens('deepseek-chat')).toBe(8000);
-      expect(getModelMaxOutputTokens('deepseek-chat', EModelEndpoint.openAI)).toBe(8000);
-      expect(getModelMaxOutputTokens('deepseek-chat', EModelEndpoint.custom)).toBe(8000);
+      const expected = maxOutputTokensMap[EModelEndpoint.openAI]['deepseek-chat'];
+      expect(getModelMaxOutputTokens('deepseek-chat')).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-chat', EModelEndpoint.openAI)).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-chat', EModelEndpoint.custom)).toBe(expected);
     });
 
     test('should return correct max output tokens for deepseek-reasoner', () => {
-      expect(getModelMaxOutputTokens('deepseek-reasoner')).toBe(64000);
-      expect(getModelMaxOutputTokens('deepseek-reasoner', EModelEndpoint.openAI)).toBe(64000);
-      expect(getModelMaxOutputTokens('deepseek-reasoner', EModelEndpoint.custom)).toBe(64000);
+      const expected = maxOutputTokensMap[EModelEndpoint.openAI]['deepseek-reasoner'];
+      expect(getModelMaxOutputTokens('deepseek-reasoner')).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-reasoner', EModelEndpoint.openAI)).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-reasoner', EModelEndpoint.custom)).toBe(expected);
     });
 
     test('should return correct max output tokens for deepseek-r1', () => {
-      expect(getModelMaxOutputTokens('deepseek-r1')).toBe(64000);
-      expect(getModelMaxOutputTokens('deepseek-r1', EModelEndpoint.openAI)).toBe(64000);
+      const expected = maxOutputTokensMap[EModelEndpoint.openAI]['deepseek-r1'];
+      expect(getModelMaxOutputTokens('deepseek-r1')).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-r1', EModelEndpoint.openAI)).toBe(expected);
     });
 
     test('should return correct max output tokens for deepseek base pattern', () => {
-      expect(getModelMaxOutputTokens('deepseek')).toBe(8000);
-      expect(getModelMaxOutputTokens('deepseek-v3')).toBe(8000);
+      const expected = maxOutputTokensMap[EModelEndpoint.openAI]['deepseek'];
+      expect(getModelMaxOutputTokens('deepseek')).toBe(expected);
+      expect(getModelMaxOutputTokens('deepseek-v3')).toBe(expected);
     });
 
     test('should handle DeepSeek models with provider prefixes for max output tokens', () => {
-      expect(getModelMaxOutputTokens('deepseek/deepseek-chat')).toBe(8000);
-      expect(getModelMaxOutputTokens('openrouter/deepseek-reasoner')).toBe(64000);
+      expect(getModelMaxOutputTokens('deepseek/deepseek-chat')).toBe(
+        maxOutputTokensMap[EModelEndpoint.openAI]['deepseek-chat'],
+      );
+      expect(getModelMaxOutputTokens('openrouter/deepseek-reasoner')).toBe(
+        maxOutputTokensMap[EModelEndpoint.openAI]['deepseek-reasoner'],
+      );
     });
   });
 
@@ -796,68 +824,90 @@ describe('Meta Models Tests', () => {
 describe('Grok Model Tests - Tokens', () => {
   describe('getModelMaxTokens', () => {
     test('should return correct tokens for Grok vision models', () => {
-      expect(getModelMaxTokens('grok-2-vision-1212')).toBe(32768);
-      expect(getModelMaxTokens('grok-2-vision')).toBe(32768);
-      expect(getModelMaxTokens('grok-2-vision-latest')).toBe(32768);
+      const grok2VisionTokens = maxTokensMap[EModelEndpoint.openAI]['grok-2-vision'];
+      expect(getModelMaxTokens('grok-2-vision-1212')).toBe(grok2VisionTokens);
+      expect(getModelMaxTokens('grok-2-vision')).toBe(grok2VisionTokens);
+      expect(getModelMaxTokens('grok-2-vision-latest')).toBe(grok2VisionTokens);
     });
 
     test('should return correct tokens for Grok beta models', () => {
-      expect(getModelMaxTokens('grok-vision-beta')).toBe(8192);
-      expect(getModelMaxTokens('grok-beta')).toBe(131072);
+      expect(getModelMaxTokens('grok-vision-beta')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-vision-beta'],
+      );
+      expect(getModelMaxTokens('grok-beta')).toBe(maxTokensMap[EModelEndpoint.openAI]['grok-beta']);
     });
 
     test('should return correct tokens for Grok text models', () => {
-      expect(getModelMaxTokens('grok-2-1212')).toBe(131072);
-      expect(getModelMaxTokens('grok-2')).toBe(131072);
-      expect(getModelMaxTokens('grok-2-latest')).toBe(131072);
+      const grok2Tokens = maxTokensMap[EModelEndpoint.openAI]['grok-2'];
+      expect(getModelMaxTokens('grok-2-1212')).toBe(grok2Tokens);
+      expect(getModelMaxTokens('grok-2')).toBe(grok2Tokens);
+      expect(getModelMaxTokens('grok-2-latest')).toBe(grok2Tokens);
     });
 
     test('should return correct tokens for Grok 3 series models', () => {
-      expect(getModelMaxTokens('grok-3')).toBe(131072);
-      expect(getModelMaxTokens('grok-3-fast')).toBe(131072);
-      expect(getModelMaxTokens('grok-3-mini')).toBe(131072);
-      expect(getModelMaxTokens('grok-3-mini-fast')).toBe(131072);
+      expect(getModelMaxTokens('grok-3')).toBe(maxTokensMap[EModelEndpoint.openAI]['grok-3']);
+      expect(getModelMaxTokens('grok-3-fast')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-fast'],
+      );
+      expect(getModelMaxTokens('grok-3-mini')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-mini'],
+      );
+      expect(getModelMaxTokens('grok-3-mini-fast')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-mini-fast'],
+      );
     });
 
     test('should return correct tokens for Grok 4 model', () => {
-      expect(getModelMaxTokens('grok-4-0709')).toBe(256000);
+      expect(getModelMaxTokens('grok-4-0709')).toBe(maxTokensMap[EModelEndpoint.openAI]['grok-4']);
     });
 
     test('should return correct tokens for Grok 4 Fast and Grok 4.1 Fast models', () => {
-      expect(getModelMaxTokens('grok-4-fast')).toBe(2000000);
-      expect(getModelMaxTokens('grok-4-1-fast-reasoning')).toBe(2000000);
-      expect(getModelMaxTokens('grok-4-1-fast-non-reasoning')).toBe(2000000);
+      const grok4FastTokens = maxTokensMap[EModelEndpoint.openAI]['grok-4-fast'];
+      const grok41FastTokens = maxTokensMap[EModelEndpoint.openAI]['grok-4-1-fast'];
+      expect(getModelMaxTokens('grok-4-fast')).toBe(grok4FastTokens);
+      expect(getModelMaxTokens('grok-4-1-fast-reasoning')).toBe(grok41FastTokens);
+      expect(getModelMaxTokens('grok-4-1-fast-non-reasoning')).toBe(grok41FastTokens);
     });
 
     test('should return correct tokens for Grok Code Fast model', () => {
-      expect(getModelMaxTokens('grok-code-fast-1')).toBe(256000);
+      expect(getModelMaxTokens('grok-code-fast-1')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-code-fast'],
+      );
     });
 
     test('should handle partial matches for Grok models with prefixes', () => {
-      // Vision models should match before general models
-      expect(getModelMaxTokens('xai/grok-2-vision-1212')).toBe(32768);
-      expect(getModelMaxTokens('xai/grok-2-vision')).toBe(32768);
-      expect(getModelMaxTokens('xai/grok-2-vision-latest')).toBe(32768);
-      // Beta models
-      expect(getModelMaxTokens('xai/grok-vision-beta')).toBe(8192);
-      expect(getModelMaxTokens('xai/grok-beta')).toBe(131072);
-      // Text models
-      expect(getModelMaxTokens('xai/grok-2-1212')).toBe(131072);
-      expect(getModelMaxTokens('xai/grok-2')).toBe(131072);
-      expect(getModelMaxTokens('xai/grok-2-latest')).toBe(131072);
-      // Grok 3 models
-      expect(getModelMaxTokens('xai/grok-3')).toBe(131072);
-      expect(getModelMaxTokens('xai/grok-3-fast')).toBe(131072);
-      expect(getModelMaxTokens('xai/grok-3-mini')).toBe(131072);
-      expect(getModelMaxTokens('xai/grok-3-mini-fast')).toBe(131072);
-      // Grok 4 model
-      expect(getModelMaxTokens('xai/grok-4-0709')).toBe(256000);
-      // Grok 4 Fast and 4.1 Fast models
-      expect(getModelMaxTokens('xai/grok-4-fast')).toBe(2000000);
-      expect(getModelMaxTokens('xai/grok-4-1-fast-reasoning')).toBe(2000000);
-      expect(getModelMaxTokens('xai/grok-4-1-fast-non-reasoning')).toBe(2000000);
-      // Grok Code Fast model
-      expect(getModelMaxTokens('xai/grok-code-fast-1')).toBe(256000);
+      const grok2VisionTokens = maxTokensMap[EModelEndpoint.openAI]['grok-2-vision'];
+      const grokVisionBetaTokens = maxTokensMap[EModelEndpoint.openAI]['grok-vision-beta'];
+      const grokBetaTokens = maxTokensMap[EModelEndpoint.openAI]['grok-beta'];
+      const grok2Tokens = maxTokensMap[EModelEndpoint.openAI]['grok-2'];
+      const grok3Tokens = maxTokensMap[EModelEndpoint.openAI]['grok-3'];
+      const grok4Tokens = maxTokensMap[EModelEndpoint.openAI]['grok-4'];
+      const grok4FastTokens = maxTokensMap[EModelEndpoint.openAI]['grok-4-fast'];
+      const grok41FastTokens = maxTokensMap[EModelEndpoint.openAI]['grok-4-1-fast'];
+      const grokCodeFastTokens = maxTokensMap[EModelEndpoint.openAI]['grok-code-fast'];
+      expect(getModelMaxTokens('xai/grok-2-vision-1212')).toBe(grok2VisionTokens);
+      expect(getModelMaxTokens('xai/grok-2-vision')).toBe(grok2VisionTokens);
+      expect(getModelMaxTokens('xai/grok-2-vision-latest')).toBe(grok2VisionTokens);
+      expect(getModelMaxTokens('xai/grok-vision-beta')).toBe(grokVisionBetaTokens);
+      expect(getModelMaxTokens('xai/grok-beta')).toBe(grokBetaTokens);
+      expect(getModelMaxTokens('xai/grok-2-1212')).toBe(grok2Tokens);
+      expect(getModelMaxTokens('xai/grok-2')).toBe(grok2Tokens);
+      expect(getModelMaxTokens('xai/grok-2-latest')).toBe(grok2Tokens);
+      expect(getModelMaxTokens('xai/grok-3')).toBe(grok3Tokens);
+      expect(getModelMaxTokens('xai/grok-3-fast')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-fast'],
+      );
+      expect(getModelMaxTokens('xai/grok-3-mini')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-mini'],
+      );
+      expect(getModelMaxTokens('xai/grok-3-mini-fast')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['grok-3-mini-fast'],
+      );
+      expect(getModelMaxTokens('xai/grok-4-0709')).toBe(grok4Tokens);
+      expect(getModelMaxTokens('xai/grok-4-fast')).toBe(grok4FastTokens);
+      expect(getModelMaxTokens('xai/grok-4-1-fast-reasoning')).toBe(grok41FastTokens);
+      expect(getModelMaxTokens('xai/grok-4-1-fast-non-reasoning')).toBe(grok41FastTokens);
+      expect(getModelMaxTokens('xai/grok-code-fast-1')).toBe(grokCodeFastTokens);
     });
   });
 
@@ -1062,6 +1112,56 @@ describe('Claude Model Tests', () => {
       expect(matchModelName(model, EModelEndpoint.anthropic)).toBe(expectedModel);
     });
   });
+
+  it('should return correct context length for Claude Opus 4.6 (1M)', () => {
+    expect(getModelMaxTokens('claude-opus-4-6', EModelEndpoint.anthropic)).toBe(
+      maxTokensMap[EModelEndpoint.anthropic]['claude-opus-4-6'],
+    );
+    expect(getModelMaxTokens('claude-opus-4-6')).toBe(
+      maxTokensMap[EModelEndpoint.anthropic]['claude-opus-4-6'],
+    );
+  });
+
+  it('should return correct max output tokens for Claude Opus 4.6 (128K)', () => {
+    const { getModelMaxOutputTokens } = require('@librechat/api');
+    expect(getModelMaxOutputTokens('claude-opus-4-6', EModelEndpoint.anthropic)).toBe(
+      maxOutputTokensMap[EModelEndpoint.anthropic]['claude-opus-4-6'],
+    );
+  });
+
+  it('should handle Claude Opus 4.6 model name variations', () => {
+    const modelVariations = [
+      'claude-opus-4-6',
+      'claude-opus-4-6-20250801',
+      'claude-opus-4-6-latest',
+      'anthropic/claude-opus-4-6',
+      'claude-opus-4-6/anthropic',
+      'claude-opus-4-6-preview',
+    ];
+
+    modelVariations.forEach((model) => {
+      const modelKey = findMatchingPattern(model, maxTokensMap[EModelEndpoint.anthropic]);
+      expect(modelKey).toBe('claude-opus-4-6');
+      expect(getModelMaxTokens(model, EModelEndpoint.anthropic)).toBe(
+        maxTokensMap[EModelEndpoint.anthropic]['claude-opus-4-6'],
+      );
+    });
+  });
+
+  it('should match model names correctly for Claude Opus 4.6', () => {
+    const modelVariations = [
+      'claude-opus-4-6',
+      'claude-opus-4-6-20250801',
+      'claude-opus-4-6-latest',
+      'anthropic/claude-opus-4-6',
+      'claude-opus-4-6/anthropic',
+      'claude-opus-4-6-preview',
+    ];
+
+    modelVariations.forEach((model) => {
+      expect(matchModelName(model, EModelEndpoint.anthropic)).toBe('claude-opus-4-6');
+    });
+  });
 });
 
 describe('Moonshot/Kimi Model Tests', () => {
@@ -1329,44 +1429,80 @@ describe('Qwen3 Model Tests', () => {
 describe('GLM Model Tests (Zhipu AI)', () => {
   describe('getModelMaxTokens', () => {
     test('should return correct tokens for GLM models', () => {
-      expect(getModelMaxTokens('glm-4.6')).toBe(200000);
-      expect(getModelMaxTokens('glm-4.5v')).toBe(66000);
-      expect(getModelMaxTokens('glm-4.5-air')).toBe(131000);
-      expect(getModelMaxTokens('glm-4.5')).toBe(131000);
-      expect(getModelMaxTokens('glm-4-32b')).toBe(128000);
-      expect(getModelMaxTokens('glm-4')).toBe(128000);
-      expect(getModelMaxTokens('glm4')).toBe(128000);
+      expect(getModelMaxTokens('glm-4.6')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.6']);
+      expect(getModelMaxTokens('glm-4.5v')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.5v']);
+      expect(getModelMaxTokens('glm-4.5-air')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
+      expect(getModelMaxTokens('glm-4.5')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.5']);
+      expect(getModelMaxTokens('glm-4-32b')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4-32b']);
+      expect(getModelMaxTokens('glm-4')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4']);
+      expect(getModelMaxTokens('glm4')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm4']);
     });
 
     test('should handle partial matches for GLM models with provider prefixes', () => {
-      expect(getModelMaxTokens('z-ai/glm-4.6')).toBe(200000);
-      expect(getModelMaxTokens('z-ai/glm-4.5')).toBe(131000);
-      expect(getModelMaxTokens('z-ai/glm-4.5-air')).toBe(131000);
-      expect(getModelMaxTokens('z-ai/glm-4.5v')).toBe(66000);
-      expect(getModelMaxTokens('z-ai/glm-4-32b')).toBe(128000);
+      expect(getModelMaxTokens('z-ai/glm-4.6')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.6'],
+      );
+      expect(getModelMaxTokens('z-ai/glm-4.5')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5'],
+      );
+      expect(getModelMaxTokens('z-ai/glm-4.5-air')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
+      expect(getModelMaxTokens('z-ai/glm-4.5v')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5v'],
+      );
+      expect(getModelMaxTokens('z-ai/glm-4-32b')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4-32b'],
+      );
 
-      expect(getModelMaxTokens('zai/glm-4.6')).toBe(200000);
-      expect(getModelMaxTokens('zai/glm-4.5')).toBe(131000);
-      expect(getModelMaxTokens('zai/glm-4.5-air')).toBe(131000);
-      expect(getModelMaxTokens('zai/glm-4.5v')).toBe(66000);
+      expect(getModelMaxTokens('zai/glm-4.6')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.6']);
+      expect(getModelMaxTokens('zai/glm-4.5')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.5']);
+      expect(getModelMaxTokens('zai/glm-4.5-air')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
+      expect(getModelMaxTokens('zai/glm-4.5v')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5v'],
+      );
 
-      expect(getModelMaxTokens('zai-org/GLM-4.6')).toBe(200000);
-      expect(getModelMaxTokens('zai-org/GLM-4.5')).toBe(131000);
-      expect(getModelMaxTokens('zai-org/GLM-4.5-Air')).toBe(131000);
-      expect(getModelMaxTokens('zai-org/GLM-4.5V')).toBe(66000);
-      expect(getModelMaxTokens('zai-org/GLM-4-32B-0414')).toBe(128000);
+      expect(getModelMaxTokens('zai-org/GLM-4.6')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.6'],
+      );
+      expect(getModelMaxTokens('zai-org/GLM-4.5')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5'],
+      );
+      expect(getModelMaxTokens('zai-org/GLM-4.5-Air')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
+      expect(getModelMaxTokens('zai-org/GLM-4.5V')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5v'],
+      );
+      expect(getModelMaxTokens('zai-org/GLM-4-32B-0414')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4-32b'],
+      );
     });
 
     test('should handle GLM model variations with suffixes', () => {
-      expect(getModelMaxTokens('glm-4.6-fp8')).toBe(200000);
-      expect(getModelMaxTokens('zai-org/GLM-4.6-FP8')).toBe(200000);
-      expect(getModelMaxTokens('zai-org/GLM-4.5-Air-FP8')).toBe(131000);
+      expect(getModelMaxTokens('glm-4.6-fp8')).toBe(maxTokensMap[EModelEndpoint.openAI]['glm-4.6']);
+      expect(getModelMaxTokens('zai-org/GLM-4.6-FP8')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.6'],
+      );
+      expect(getModelMaxTokens('zai-org/GLM-4.5-Air-FP8')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
     });
 
     test('should prioritize more specific GLM patterns', () => {
-      expect(getModelMaxTokens('glm-4.5-air-custom')).toBe(131000);
-      expect(getModelMaxTokens('glm-4.5-custom')).toBe(131000);
-      expect(getModelMaxTokens('glm-4.5v-custom')).toBe(66000);
+      expect(getModelMaxTokens('glm-4.5-air-custom')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5-air'],
+      );
+      expect(getModelMaxTokens('glm-4.5-custom')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5'],
+      );
+      expect(getModelMaxTokens('glm-4.5v-custom')).toBe(
+        maxTokensMap[EModelEndpoint.openAI]['glm-4.5v'],
+      );
     });
   });