Source: https://ailoot.ai/models/en/ernie-x1-turbo-32k/ Canonical: https://ailoot.ai/models/en/ernie-x1-turbo-32k/ Language: en # ernie-x1-turbo-32k [Markdown](https://ailoot.ai/models/en/ernie-x1-turbo-32k/index.md) · [JSON](https://ailoot.ai/models/en/ernie-x1-turbo-32k/facts.json) ## [ernie-x1-turbo-32k](https://ailoot.ai/models/en/ernie-x1-turbo-32k/) ERNIE X1 Turbo is Baidu’s reasoning profile for mathematical, logical and tool-connected text tasks. Its AI Studio specification distinguishes 32K context, 24K maximum input, final-output settings up to 16,384 tokens and a separate 16K thinking length. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `ernie-x1-turbo-32k`](https://ailoot.ai/models/en/ernie-x1-turbo-32k/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `ernie-x1-turbo-32k`](https://ailoot.ai/models/en/ernie-x1-turbo-32k/#method-1) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. ERNIE X1 Turbo is Baidu’s reasoning-model profile for questions requiring extended analysis, logical problem solving and tool-connected text work. The maker identifies Turbo as an enhanced version of the X1 line. Its documented 32K producer profile remains separate from ERNIE X1.1 Preview and ERNIE 5.0 Thinking, which have different input and generation budgets. #### Prepare a complete reasoning problem Supply the question, given values, constraints and the kind of result needed. A mathematical task can request a solution whose decisive calculation can be checked; a planning task can compare alternatives against stated conditions. Keep assumptions separate from supplied facts and identify what remains unknown. This gives the reasoning process a bounded problem rather than permission to invent missing requirements. For a technical question, include the relevant source passage or code fragment and the behavior to investigate. Ask for a concrete counterexample or explanation if two implementations differ. The useful artifact should make the conclusion and its governing conditions clear. Check important values and the proposed behavior against the source instead of treating a lengthy generated analysis as proof of correctness. #### Context, final output and thinking budgets The producer’s AI Studio model table lists a 32K context, a 24K maximum input and a final-output setting from 2 to 16,384 tokens, with 2K as the documented default. It separately lists a 16K thinking length for X1 Turbo. These fields have distinct roles; choose the source material and requested final artifact accordingly rather than interpreting the context number as a promise of an equally long answer. Keep the final answer separate from any reasoning material returned by the producer flow. A comparison can place the recommendation first and retain the conditions that establish it. A code-related response can identify the exact input case and expected result. Preserve the original question and evidence with the completed artifact so a follow-up can examine one unresolved step. #### Functions and current information The manufacturer’s feature index lists X1 Turbo for function calling and search-enhanced tasks. Define any application function’s purpose and arguments, then return the actual result when the model requests it. The model supplies a structured request; the surrounding application performs the operation. A proposed lookup is separate from a retrieved record, and a requested action is separate from a completed outcome. For a source-based question, keep the retrieved material with the answer and check whether it establishes the claim. A dynamic fact should come from current evidence rather than an unsupported memory-based response. Treat the function and search integration as the producer’s connected workflow, with the actual returned evidence determining what the answer can responsibly conclude. #### Keep the X1 Turbo edition with the result The 32K X1 Turbo profile is a reasoning text workflow, not the broader native multimodal generation specification of another ERNIE family. Its input, final-output and thinking budgets remain those of the selected producer profile. A useful handoff can contain the solution, comparison or tool-informed answer together with the source passages and completed operation results. This supports a focused continuation while preserving the original task and its uncertainty. **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `ernie-x1-turbo-32k` [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=chat) Baidu /`ernie-x1-turbo-32k` ### Overview ERNIE X1 Turbo is Baidu’s reasoning profile for mathematical, logical and tool-connected text tasks. Its AI Studio specification distinguishes 32K context, 24K maximum input, final-output settings up to 16,384 tokens and a separate 16K thinking length. Accepted inputs Text Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` Input types `text` Output types `text` Output format json or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits ernie-x1-turbo-32k | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Model-specific | Default— | | Field`messages[].content` | Type`string` | RequirementOptional | Values & limits Applies when: Text conversation | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF8 text `messages[].content` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`material_types` | Contracttext | | Parameter`messages` | Contractmessage array with role/content shape | | Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred | | Parameter`optional_controls` | Contractstream | | Parameter`control_scope` | ContractText conversation; stream is a boolean and defaults to false. | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_input_binding` | Contractnull | Method aliases: `/chat/completions` Lifecycle: `synchronous_or_streamed` Send exact model ID with messages. Context length is not an output cap. A text file may supply literal text; image/audio/file-object inputs need exact binding. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.00033** / 1K tokens Output · Output tokens **$0.00132** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curlPython SDK ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model":"ernie-x1-turbo-32k","messages":[{"role":"user","content":"hello"}],"stream":false}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path r=json.loads(Path("result.json").read_text()) for x in r.get("choices",[]): if x.get("message",{}).get("content"): print(x["message"]["content"]) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) result=client.chat.completions.create(model="ernie-x1-turbo-32k", messages=[{"role":"user","content":"hello"}], stream=False) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(result.to_dict(mode="json")) ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat json or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`choices[].index` | Typeinteger | | Field`choices[].message.content` | Typestring\|null | | Field`choices[].message.tool_calls` | Typetool\_call\[\] | | Field`choices[].message.reasoning_content` | Typestring | | Field`choices[].message.reasoning_details` | Typeobject\|array | | Field`choices[].finish_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=chat) #### Method path aliases `/chat/completions` #### Related caller IDs [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `ernie-x1-turbo-32k` [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=messages) Baidu /`ernie-x1-turbo-32k` ### Overview ERNIE X1 Turbo is Baidu’s reasoning profile for mathematical, logical and tool-connected text tasks. Its AI Studio specification distinguishes 32K context, 24K maximum input, final-output settings up to 16,384 tokens and a separate 16K thinking length. Accepted inputs Text Results json · sse ### Available in Ailoot · inputs & methods **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` Input types `text` Output types `json`, `sse` Output format JSON or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits ernie-x1-turbo-32k | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits user, assistant | Default— | | Field`messages[].content` | Type`string` | RequirementRequired | Values & limits | Default— | | Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF-8 text `messages[].content string` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages, max\_tokens | | Parameter`optional_controls` | Contractstream | | Parameter`material_types` | Contracttext | | Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. | Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.00033** / 1K tokens Output · Output tokens **$0.00132** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "ernie-x1-turbo-32k", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'ernie-x1-turbo-32k', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8') PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \ -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) PY_RESULT ``` ```python import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'ernie-x1-turbo-32k', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text from urllib.request import Request, urlopen from urllib.error import HTTPError api_key = os.environ['AILOOT_API_KEY'] if not api_key.strip(): raise SystemExit('Set AILOOT_API_KEY before making a request') headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'} headers['x-api-key'] = api_key request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST') try: with urlopen(request, timeout=120) as response: raw = response.read() Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8') except HTTPError as error: Path('result.json').write_bytes(error.read()) Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8') raise Path('result.json').write_bytes(raw) import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) ``` ### Receive and save results #### Ailoot Ultra · Anthropic Messages JSON or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`id` | Typestring | | Field`model` | Typestring | | Field`content[]` | Typetext\|thinking\|tool\_use\|other content block | | Field`stop_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=messages) #### Method path aliases `/chat/completions` #### Related caller IDs [`ernie-x1-turbo-32k`](https://ailoot.ai/models/en/#m=ernie-x1-turbo-32k&route=Ultra&caller=ernie-x1-turbo-32k&fmt=messages) FAQ · ernie-x1-turbo-32k What inputs and results are available? `POST /v1/chat/completions` · `ernie-x1-turbo-32k`: `text` → `text` `POST /v1/messages` · `ernie-x1-turbo-32k`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `ernie-x1-turbo-32k`: `model`, `messages`, `stream` `POST /v1/messages` · `ernie-x1-turbo-32k`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `ernie-x1-turbo-32k`: `messages.min_items`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. 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