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* feat: add BigModel provider with native Chat, Responses, and Messages APIs Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * test(providers): make BigModel unit tests a package Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * docs(providers): clarify BigModel parameter interface Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * fix(bigmodel): keep the Coding Plan base, surface Responses auth errors, and scope routes Chat Completions keeps a GLM Coding Plan api_base. Responses raises the status from BigModel's success=false body instead of returning an empty 200, and a JSON body answering a stream request is replayed as a one-shot stream. Messages streams keep ping and [DONE] lines. bigmodel leaves openai_compatible_providers so speech, image, and transcription routes send it no request * fix(responses): replay a JSON body on a stream request only when it has a Content-Length A gateway that labels a chunked SSE stream as application/json kept streaming before the replay, so only a sized body is read in full * fix(bigmodel): scope the JSON stream replay to BigModel, map its business error codes, and echo requested tools * fix(bigmodel): forward Codex's include and parallel_tool_calls, keep the OpenAI org header off BigModel, and pass unchanged Messages stream events through * fix(bigmodel): map disabled thinking to low effort, keep Messages temperature, raise context window and content policy errors, and default parallel_tool_calls to true * fix(bigmodel): keep the caller's reasoning_effort when Messages rewrites disabled thinking * fix(bigmodel): keep context-window and content-policy errors on streamed Responses replays --------- Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
63 lines
3.3 KiB
Markdown
63 lines
3.3 KiB
Markdown
# BigModel (Zhipu AI)
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Use `bigmodel/` for the domestic BigModel platform and `zai/` for international Z.AI. Each platform uses its own credentials and default URL
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```yaml
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model_list:
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- model_name: glm
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litellm_params:
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model: bigmodel/glm-5.3
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api_key: os.environ/BIGMODEL_API_KEY
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api_base: https://open.bigmodel.cn
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```
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One model configuration supports all three native APIs. The provider selects the upstream endpoint from the requested API
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| Proxy endpoint | BigModel endpoint |
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| --- | --- |
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| `/v1/chat/completions` | `/api/paas/v4/chat/completions` |
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| `/v1/responses` | `/api/v1/responses` |
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| `/v1/messages` | `/api/anthropic/v1/messages` |
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`api_base` is optional and defaults to `https://open.bigmodel.cn`. Set `BIGMODEL_API_BASE` to override the default, or pass an explicit `api_base` to override the environment value. Existing API-specific base URLs and complete endpoint URLs are normalized, preserving a custom proxy's path prefix
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A GLM Coding Plan key uses `https://open.bigmodel.cn/api/coding/paas/v4` as its `api_base`, since Coding Plan and standard keys do not work on each other's endpoints. Chat Completions then calls the Coding Plan endpoint, and Responses and Messages keep the endpoints in the table above
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The Admin UI lists BigModel (Zhipu AI), displays its official logo, and provides a required password API Key field and an optional API Base field with the default URL. Authentication uses an explicit `api_key` or `BIGMODEL_API_KEY`
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```python
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import litellm
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chat = litellm.completion(
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model="bigmodel/glm-5.3",
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messages=[{"role": "user", "content": "Hello"}],
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thinking={"type": "enabled"},
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reasoning_effort="low",
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)
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response = litellm.responses(
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model="bigmodel/glm-5.3",
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input="Hello",
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reasoning={"effort": "low"},
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store=True,
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)
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async def messages():
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return await litellm.anthropic.messages.acreate(
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model="bigmodel/glm-5.3",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Hello"}],
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thinking={"type": "adaptive"},
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output_config={"effort": "low"},
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)
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```
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Each API retains its native response format and streaming events. The native Messages SDK entry point is asynchronous, as is the proxy's `/v1/messages` route
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GLM-5.3, GLM-5.3-Flash, and GLM-5.3-FlashX use always-on thinking. GLM-5.3 supports `low`, `high`, and `max` reasoning effort. Messages preserves native `thinking`, `output_config`, and `reasoning_effort` parameters
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Set `store=True` when creating a Responses request if you need continuation, retrieval, or deletion. Use the response ID returned by LiteLLM. BigModel documents a seven-day retention period and does not support cancel, compact, native WebSocket, or file_search
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The model catalog and packaged backup include GLM-5.3, GLM-5.3-Flash, and GLM-5.3-FlashX. Prices are expressed in USD/token using a static 7.0 CNY/USD conversion, with the source and verification date in each entry's metadata. Use existing `litellm_params` cost fields for contract-specific deployment prices
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API references: [Chat Completions](https://docs.bigmodel.cn/cn/guide/develop/openai/introduction), [Responses](https://docs.bigmodel.cn/cn/guide/develop/responses/introduction), [Messages](https://docs.bigmodel.cn/cn/guide/develop/claude/introduction), and [pricing](https://docs.bigmodel.cn/cn/guide/start/pricing), checked on 2026-10-27
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