bug: GLM 4.7 erroneously below GLM 4.6V (#931)

[tawnymanticore]

What does this PR do?

[
bug: GLM 4.7 erroneously below GLM 4.6V

Checklists

• Tests have been run locally and passed
• New tests have been added to any work in /lib

Summary by CodeRabbit

• New Features
  • GLM-4.7 model now available through three providers: OpenRouter, SiliconFlow CN, and Cerebras
  • Supports structured output formatting and advanced reasoning capabilities
  • Expands AI model options for enhanced task processing

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[coderabbitai]

Walkthrough

The pull request relocates the GLM-4.7 model configuration within the model registry file, repositioning it to a new location and updating its provider definitions (OpenRouter, SiliconFlow CN, and Cerebras) with adjusted model IDs and structured output modes.

Changes

Cohort / File(s)	Summary
GLM-4.7 Model Registry Relocation libs/core/kiln_ai/adapters/ml_model_list.py	Removes existing GLM-4.7 model entry and adds it to a new position with updated provider configurations: OpenRouter (json_instructions mode), SiliconFlow CN (json_instructions mode with reasoning_optional_for_structured_output), and Cerebras (json_schema mode). All three providers configured with reasoning capabilities enabled.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

Possibly related PRs

• Adding support for GLM 4.7 #923: Modifies the same GLM-4.7 model entry in ml_model_list.py, adjusting provider configurations.
• 4.7 should be above 4.6V #931: Identical code-level change relocating GLM-4.7 model entry above GLM-4.6 with duplicate removal.
• add models (Minimax M2.1, Bytedance, Mistral) #926: Updates GLM-4.7 providers (Cerebras and OpenRouter) in the same model registry file.

Suggested reviewers

• scosman
• leonardmq

Poem

🐰 The GLM-4.7 hops to a brighter place,
New providers dance with structured grace,
Cerebras, OpenRouter, Silicon's gleam—
A reorganized model registry dream! 🌟

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 inconclusive)

Check name	Status	Explanation	Resolution
Description check	❓ Inconclusive	The description is missing the 'Related Issues' section and lacks detail about the actual change, though required checklists are completed.	Add the 'Related Issues' section referencing issue #931 and expand 'What does this PR do?' to explain the specific reordering change.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly describes the main change: relocating GLM 4.7 to its correct position above GLM 4.6V in the model list.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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✨ Finishing touches

• 📝 Generate docstrings

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📜 Recent review details

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Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 811194a and 133e01b.

📒 Files selected for processing (1)

• libs/core/kiln_ai/adapters/ml_model_list.py

🧰 Additional context used

📓 Path-based instructions (2)

libs/core/**/*.py

📄 CodeRabbit inference engine (.cursor/rules/project.mdc)

Use Python 3.10+ for the core library (libs/core) and Python 3.13 for the desktop app

Use Python 3.10+ for the core library (libs/core)

Files:

• libs/core/kiln_ai/adapters/ml_model_list.py

**/*.py

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**/*.py: Use asyncio for asynchronous operations in Python
Use Pydantic v2 (not v1) for data validation

**/*.py: Use Pydantic v2, not v1
Use asyncio for asynchronous operations in Python

Files:

• libs/core/kiln_ai/adapters/ml_model_list.py

🧠 Learnings (8)

📓 Common learnings

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:1979-1983
Timestamp: 2025-08-08T16:14:54.346Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.py, for ModelName.deepseek_r1_distill_qwen_32b on provider siliconflow_cn (model_id "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"), do not set a parser (r1_thinking/optional_r1_thinking). Verified via testing that SiliconFlow returns final outputs that parse without an R1 parser, and reasoning may be omitted under structured output; rely on reasoning_optional_for_structured_output=True.

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:0-0
Timestamp: 2025-07-16T09:37:39.816Z
Learning: The `glm_z1_rumination_32b_0414` model was intentionally removed from the built_in_models list due to output formatting issues: output was duplicated in both `output` and `reasoning` fields, and contained random internal JSON in the output. This model should not be re-added without addressing these formatting problems.

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:0-0
Timestamp: 2025-07-16T09:37:39.816Z
Learning: The `glm_z1_rumination_32b_0414` model was intentionally removed from the built_in_models list due to output formatting issues: output was duplicated in both `output` and `reasoning` fields, and contained random internal JSON in the output. This model should not be re-added without addressing these formatting problems.

📚 Learning: 2025-08-08T16:13:26.526Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:1875-1890
Timestamp: 2025-08-08T16:13:26.526Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.py (Python), do not blanket-add r1_thinking/optional_r1_thinking parsers for R1-style models. Parser usage is provider-specific and must be based on observed responses in tests. For PR Kiln-AI/Kiln#418, deepseek_r1_0528_distill_qwen3_8b providers were validated without needing a parser.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-08-08T16:14:54.346Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:1979-1983
Timestamp: 2025-08-08T16:14:54.346Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.py, for ModelName.deepseek_r1_distill_qwen_32b on provider siliconflow_cn (model_id "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"), do not set a parser (r1_thinking/optional_r1_thinking). Verified via testing that SiliconFlow returns final outputs that parse without an R1 parser, and reasoning may be omitted under structured output; rely on reasoning_optional_for_structured_output=True.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-08-08T16:19:20.074Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:1736-1741
Timestamp: 2025-08-08T16:19:20.074Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.py (Python), for ModelName.qwq_32b with ModelProviderName.siliconflow_cn (model_id "Qwen/QwQ-32B"), do not set a parser (r1_thinking/optional_r1_thinking). Verified via tests/responses that SiliconFlow returns outputs that parse correctly without an R1 parser. Parser usage remains provider-specific and should only be added when validated.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-07-16T09:37:39.816Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:0-0
Timestamp: 2025-07-16T09:37:39.816Z
Learning: The `glm_z1_rumination_32b_0414` model was intentionally removed from the built_in_models list due to output formatting issues: output was duplicated in both `output` and `reasoning` fields, and contained random internal JSON in the output. This model should not be re-added without addressing these formatting problems.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-08-08T15:50:45.334Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:221-228
Timestamp: 2025-08-08T15:50:45.334Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.KilnModelProvider, naming policy: keep cross-provider feature flags unprefixed (e.g., reasoning_optional_for_structured_output) to allow reuse across providers; prefix provider-specific toggles with the provider name (e.g., siliconflow_enable_thinking) when the implementation is specific and potentially unsafe to generalize.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-08-08T16:15:20.796Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 418
File: libs/core/kiln_ai/adapters/ml_model_list.py:2434-2439
Timestamp: 2025-08-08T16:15:20.796Z
Learning: In libs/core/kiln_ai/adapters/ml_model_list.py (Python), for ModelName.qwen_3_8b_no_thinking with ModelProviderName.siliconflow_cn (model_id "Qwen/Qwen3-8B"), the qwen3_style_no_think formatter is not required: SiliconFlow’s non‑thinking Qwen3‑8B returns clean output without <answer> wrappers. Formatter/parser usage is provider-specific and should be added only when verified by tests/responses.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

📚 Learning: 2025-08-22T11:15:53.584Z

Learnt from: leonardmq
Repo: Kiln-AI/Kiln PR: 413
File: libs/core/kiln_ai/adapters/embedding/embedding_registry.py:14-20
Timestamp: 2025-08-22T11:15:53.584Z
Learning: In the Kiln AI codebase, model_provider_name fields in datamodels like EmbeddingConfig are typed as ModelProviderName enum (which inherits from str, Enum), not as plain strings. Therefore, accessing .value on these fields is valid and returns the string representation of the enum value.

Applied to files:

• libs/core/kiln_ai/adapters/ml_model_list.py

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🔇 Additional comments (1)

libs/core/kiln_ai/adapters/ml_model_list.py (1)

5188-5214: Model ID patterns need verification before approval.

The GLM 4.7 ordering fix is correct, but the provider configuration has inconsistencies that require verification:

1. SiliconFlow model_id: GLM 4.7 uses "Pro/zai-org/GLM-4.7" (with Pro/ prefix), while GLM 4.6 and GLM 4.6V both use "zai-org/..." without the prefix. Verify this prefix is intentional and correct for the GLM 4.7 endpoint on SiliconFlow.

2. OpenRouter model_id: GLM 4.6 uses "z-ai/glm-4.6:exacto" (with :exacto suffix), while GLM 4.7 uses "z-ai/glm-4.7" without the suffix. Confirm whether OpenRouter dropped the suffix for newer versions or if GLM 4.7 should include it.

3. reasoning_optional_for_structured_output: Present on GLM 4.7 and GLM 4.6V SiliconFlow, but absent on GLM 4.6 SiliconFlow. Verify this is intentional based on model capabilities.

Cerebras model_ids follow the consistent zai-glm-X pattern.

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[gemini-code-assist]

Summary of Changes

Hello @tawnymanticore, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a minor bug where the GLM 4.7 model definition was incorrectly positioned after GLM 4.6V in the model list. The change reorders these entries to maintain logical versioning, ensuring the model list is consistently and correctly structured.

Highlights

• Model Definition Ordering: Corrected the order of the GLM 4.7 model definition within the ml_model_list.py file.
• Logical Consistency: Ensured that GLM 4.7 is now listed before GLM 4.6V, resolving an erroneous ordering.

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Code Review

This pull request addresses a bug where the GLM 4.7 model was incorrectly positioned after GLM 4.6V in the model list. The change moves the GLM 4.7 definition to its correct place, ensuring the models are listed in a logical, version-descending order. The fix is simple, accurate, and improves the maintainability of the model list. I've reviewed the changes and found no issues. The code is ready to be merged.

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