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Large Language Models

Guides for using Bitdeer AI's text generation models — capabilities, parameters, and advanced features.

Model capabilities

Large Language Models (LLMs) on Bitdeer AI are accessed through the Chat Completions HTTP API (POST /v1/chat/completions). They support:

  • Text Generation — Generate coherent, contextual text for any task
  • Code Generation — Write, explain, and debug code in Python, JavaScript, and 30+ languages
  • Summarization & Translation — Condense documents or translate between languages
  • Multi-turn Conversation — Maintain context across multiple user/assistant exchanges
  • Tool Use — Call external functions and APIs via Function Calling

Available models

ModelProviderModalityHighlights
zai-org/GLM-5.2Zhipu AITextCode, tool use; thinking on/off
moonshotai/Kimi-K2.6Moonshot AIText + Vision + VideoStrong Chinese + English
Qwen/Qwen3.5-397B-A17BAlibaba QwenText + Vision + VideoHybrid thinking; flagship Qwen
MiniMaxAI/MiniMax-M2.5MiniMaxTextGeneral-purpose
nvidia/NVIDIA-Nemotron-3-Super-120B-A12BNVIDIATextHigh-efficiency MoE
nvidia/Nemotron-3-Nano-Omni-30B-A3BNVIDIAText + Vision + Audio + VideoSupports audio input
deepseek-ai/DeepSeek-V4-ProDeepSeekTextLatest flagship reasoning
XiaomiMiMo/MiMo-V2.5-ProXiaomiTextGeneral-purpose
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55BNVIDIATextUltra-scale MoE

See the full list in the Model Catalog.

API integration

Use the API Reference for request and response fields. In short:

  1. Base URL: https://api-inference.bitdeer.ai/v1
  2. Send Authorization: Bearer <your Bitdeer key>

The snippet below uses the Python helper from SDKs; the same call is a JSON POST over HTTPS.

from openai import OpenAI

client = OpenAI(
    base_url="https://api-inference.bitdeer.ai/v1",
    api_key="YOUR_BITDEER_API_KEY",
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is serverless inference?"},
    ],
    stream=True,
    max_tokens=512,
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Feature guides

Key parameters

ParameterTypeDescription
modelstringRequired. The model ID from the Model Catalog.
messagesarrayRequired. The conversation history — system, user, and assistant messages.
max_tokensintegerMaximum tokens in the response.
temperaturefloatControls randomness. 0 = deterministic, 2 = highly creative. Default 1.
top_pfloatNucleus sampling. Controls cumulative probability. Default 1.
streambooleanWhether to stream the response. Default false.
stopstring or arraySequences that will terminate generation.
frequency_penaltyfloatPenalizes repeated tokens. Range [-2, 2].
presence_penaltyfloatEncourages topic diversity. Range [-2, 2].

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