Update default LLM provider models#314
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📝 WalkthroughWalkthroughUpdated default model identifiers and LLM type tags in the provider defaults within Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes Poem
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Actionable comments posted: 3
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@agents/llm_config.py`:
- Around line 176-178: The Cerebras config block currently sets llm_type to
LLMType.KIMI while agent_model is "zai-glm-4.7"; change the llm_type assignment
to LLMType.GLM so the GLM-specific behavior is used (find the block with
agent_model="zai-glm-4.7" and parsing_model="gpt-oss-120b" and replace
llm_type=LLMType.KIMI with llm_type=LLMType.GLM).
- Line 242: The agent configuration uses the wrong Gemini 3 Flash slug causing
model_not_found; update the agent_model setting in agents/llm_config.py (the
agent_model variable/keyword) to OpenRouter's preview slug
"google/gemini-3-flash-preview" wherever agent_model="google/gemini-3-flash" is
set, ensuring the non-preview Vercel AI Gateway slug is not used.
- Line 165: Replace the Anthropic alias in the parsing_model setting with the
full AWS Bedrock model identifier: locate the parsing_model parameter (currently
"claude-haiku-4-5") in llm_config.py and change its value to the Bedrock ID
"anthropic.claude-haiku-4-5-20251001-v1:0" so ChatBedrockConverse receives the
correct Bedrock model string; ensure the string format matches the existing
agent_model style (e.g., "anthropic.claude-sonnet-4-6") used earlier in the
file.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
| parsing_model="us.anthropic.claude-3-haiku-20240307-v1:0", | ||
| llm_type=LLMType.CLAUDE, | ||
| agent_model="anthropic.claude-sonnet-4-6", | ||
| parsing_model="claude-haiku-4-5", |
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🧩 Analysis chain
🌐 Web query:
AWS Bedrock Claude Haiku 4.5 model ID anthropic.claude-haiku-4-5-20251001-v1:0
💡 Result:
Yes, the model ID "anthropic.claude-haiku-4-5-20251001-v1:0" is the correct ID for AWS Bedrock's Claude Haiku 4.5 model. It is available via the bedrock-runtime endpoint and supported in numerous AWS regions for cross-region inference. Launched October 16, 2025, it supports text and image input, text output, up to 64K output tokens, and reasoning.
Citations:
- 1: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-haiku-4-5.html
- 2: https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock/
- 3: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
- 4: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
- 5: https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-5.md
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Fix AWS Bedrock Haiku model ID to use the full Bedrock identifier.
Line 165 uses "claude-haiku-4-5", which is the Anthropic API alias. ChatBedrockConverse requires the full AWS Bedrock model ID. Note that line 164's agent_model already uses the correct format ("anthropic.claude-sonnet-4-6"); parsing_model should match. The correct Bedrock Haiku 4.5 model ID is "anthropic.claude-haiku-4-5-20251001-v1:0".
Proposed fix
- parsing_model="claude-haiku-4-5",
+ parsing_model="anthropic.claude-haiku-4-5-20251001-v1:0",📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| parsing_model="claude-haiku-4-5", | |
| parsing_model="anthropic.claude-haiku-4-5-20251001-v1:0", |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@agents/llm_config.py` at line 165, Replace the Anthropic alias in the
parsing_model setting with the full AWS Bedrock model identifier: locate the
parsing_model parameter (currently "claude-haiku-4-5") in llm_config.py and
change its value to the Bedrock ID "anthropic.claude-haiku-4-5-20251001-v1:0" so
ChatBedrockConverse receives the correct Bedrock model string; ensure the string
format matches the existing agent_model style (e.g.,
"anthropic.claude-sonnet-4-6") used earlier in the file.
| agent_model="zai-glm-4.7", | ||
| parsing_model="gpt-oss-120b", | ||
| llm_type=LLMType.KIMI, |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
Cerebras zai-glm-4.7 GLM 4.7 model ID
💡 Result:
The model ID for Z.ai GLM 4.7 on Cerebras Inference is zai-glm-4.7.
Citations:
- 1: https://aimodelapis.com/providers/cerebras/cerebras-zai-glm-4-7
- 2: https://inference-docs.cerebras.ai/models/overview
- 3: https://inference-docs.cerebras.ai/resources/glm-47-migration
- 4: https://inference-docs.cerebras.ai/models/zai-glm-47
- 5: https://cerebras-inference.mintlify.app/models/zai-glm-47
- 6: https://cloudprice.net/models/cerebras/zai-glm-4.7
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Change llm_type to LLMType.GLM for the Cerebras config.
zai-glm-4.7 is a GLM model, and the from_model_name() function already maps "glm" to LLMType.GLM. Setting LLMType.KIMI risks applying Kimi-specific prompting logic to a GLM model if this field is used directly.
Proposed fix
agent_model="zai-glm-4.7",
parsing_model="gpt-oss-120b",
- llm_type=LLMType.KIMI,
+ llm_type=LLMType.GLM,📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| agent_model="zai-glm-4.7", | |
| parsing_model="gpt-oss-120b", | |
| llm_type=LLMType.KIMI, | |
| agent_model="zai-glm-4.7", | |
| parsing_model="gpt-oss-120b", | |
| llm_type=LLMType.GLM, |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@agents/llm_config.py` around lines 176 - 178, The Cerebras config block
currently sets llm_type to LLMType.KIMI while agent_model is "zai-glm-4.7";
change the llm_type assignment to LLMType.GLM so the GLM-specific behavior is
used (find the block with agent_model="zai-glm-4.7" and
parsing_model="gpt-oss-120b" and replace llm_type=LLMType.KIMI with
llm_type=LLMType.GLM).
| api_key_env="OPENROUTER_API_KEY", | ||
| agent_model="google/gemini-2.5-flash", | ||
| parsing_model="google/gemini-2.5-flash", | ||
| agent_model="google/gemini-3-flash", |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
OpenRouter Google Gemini 3 Flash Preview model ID google/gemini-3-flash-preview
💡 Result:
Yes, the model ID for Google's Gemini 3 Flash Preview on OpenRouter is "google/gemini-3-flash-preview". This is confirmed directly from OpenRouter's official model pages and documentation. It is a high-speed, cost-effective model released on December 17, 2025, with a 1.05M token context window, multimodal support (text, images, audio, video, PDFs), tool use, structured outputs, and configurable reasoning levels. Pricing is $0.50 per million input tokens and $3 per million output tokens via providers like Google Vertex and Google AI Studio.
Citations:
- 1: https://openrouter.ai/google/gemini-3-flash-preview
- 2: https://openrouter.ai/models/google/gemini-3-flash-preview
- 3: https://openrouter.ai/google/gemini-3-flash-preview/api
- 4: https://openrouter.ai/google
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Change agent_model to OpenRouter's Gemini 3 Flash slug.
OpenRouter lists Gemini 3 Flash as google/gemini-3-flash-preview; the non-preview slug is the Vercel AI Gateway model and will fail with model_not_found on OpenRouter.
Proposed fix
- agent_model="google/gemini-3-flash",
+ agent_model="google/gemini-3-flash-preview",📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| agent_model="google/gemini-3-flash", | |
| agent_model="google/gemini-3-flash-preview", |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@agents/llm_config.py` at line 242, The agent configuration uses the wrong
Gemini 3 Flash slug causing model_not_found; update the agent_model setting in
agents/llm_config.py (the agent_model variable/keyword) to OpenRouter's preview
slug "google/gemini-3-flash-preview" wherever
agent_model="google/gemini-3-flash" is set, ensuring the non-preview Vercel AI
Gateway slug is not used.
ivanmilevtues
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good to go just fix the vercel back to gpt-5-mini
| api_key_env="VERCEL_API_KEY", | ||
| agent_model="google/gemini-3-flash", | ||
| parsing_model="openai/gpt-5-mini", # Use OpenAI model for parsing to avoid trustcall compatibility issues with Gemini | ||
| parsing_model="google/gemini-3.1-flash-lite-preview", |
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this is borken, we cannot use gemini for vercel ;d
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