Google Gemini models

OpenPAI supports both the OpenAI-compatible protocol and the Gemini-native protocol (/v1beta/models/{model}:generateContent), covering Google’s latest Gemini 3 preview and the stable 2.5 series.

Gemini 3 series (current mainline)

The next-generation flagship, suited to agents, complex reasoning, realtime voice and more — currently Google’s primary series.

Model ID	Type	Positioning
`gemini-3.1-pro-preview`	Chat / Agent	Most intelligent — top-tier reasoning and agent capability for complex multi-step tasks
`gemini-3.5-flash`	Chat	Frontier-class performance rivaling larger models
`gemini-3-flash`	Chat	Frontier capability + lower cost
`gemini-3.1-flash-lite`	Chat	High performance + ultra-low cost, for high-volume lightweight tasks
`gemini-3.1-flash-live-preview`	Realtime audio	Voice-first apps, bidirectional low-latency streaming (Live API)
`gemini-3.1-flash-tts-preview`	Speech synthesis	Low-latency, natural TTS

Preview models may change their interface and pricing at any time; for production, also configure a stable fallback model. The available IDs shown in Console → Models are authoritative.

Gemini 2.5 series (stable)

Model ID	Context	Positioning
`gemini-2.5-pro`	1M	Strongest general reasoning and code
`gemini-2.5-flash`	1M	Best value for reasoning tasks
`gemini-2.5-flash-lite`	1M	Fastest, cheapest multimodal option
`gemini-2.5-flash-native-audio-preview`	1M	Sub-second native audio streaming

Thinking and reasoning effort

The Gemini 2.5 / 3 series supports an explicit thinking budget. Both ways work:

Via parameter

resp = client.models.generate_content(
    model="gemini-2.5-pro",
    contents="Prove Goldbach's conjecture",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=8000),
    ),
)

Via model ID suffix

OpenPAI passes through the following model ID aliases, mapping them internally to the corresponding thinking config:

Model ID	Behavior
`gemini-2.5-pro-thinking`	Default medium thinking budget
`gemini-2.5-pro-thinking-128`	Force a 128-token thinking budget
`gemini-2.5-flash-thinking`	Flash + thinking on
`gemini-2.5-flash-nothinking`	Flash + thinking off (cheaper)
`gemini-2.5-pro-low/medium/high`	Progressively higher reasoning effort
`gemini-3.1-pro-preview-high`	Gemini 3 Pro, maximum reasoning budget

Multimodal generation

Model ID	Use case
`nano-banana-pro`	Top-tier visual creation / 4K professional design
`nano-banana-2`	Efficient image generation / editing
`gemini-2.5-flash-image`	Native image generation / editing
`imagen-4`	Ultra-fast text-to-image (2K)
`veo-3.1`	Video generation (with native audio); availability subject to the console

Example:

curl "https://openp.ai/v1beta/models/gemini-2.5-flash-image:generateContent" \
  -H "x-goog-api-key: $OPENPAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"role":"user","parts":[{"text":"A red panda surfing, cartoon style"}]}],
    "generationConfig": {"responseModalities": ["IMAGE"]}
  }'

Embedding models

Model ID	Dimensions	Positioning
`gemini-embedding-2`	3072 (reducible)	Latest multimodal embedding, first choice for semantic retrieval
`gemini-embedding-001`	3072 (reducible)	Text embedding, RAG / classification
`text-embedding-004`	768	Older, for compatibility

Examples

OpenAI-compatible protocol

from openai import OpenAI
client = OpenAI(api_key="sk-...", base_url="https://openp.ai/v1")

resp = client.chat.completions.create(
    model="gemini-3.1-pro-preview",
    messages=[{"role": "user", "content": "Explain the Fourier transform"}],
)
print(resp.choices[0].message.content)

Gemini native protocol

curl "https://openp.ai/v1beta/models/gemini-3.1-pro-preview:generateContent" \
  -H "x-goog-api-key: $OPENPAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"role":"user","parts":[{"text":"hello"}]}],
    "generationConfig": { "temperature": 0.7, "maxOutputTokens": 2048 }
  }'

google-genai SDK

from google import genai

client = genai.Client(
    api_key="sk-XXXXXXXXXXXXXXXX",
    http_options={"base_url": "https://openp.ai"},
)

resp = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Give me 3 travel vlog titles",
)
print(resp.text)

Vision and multimodal input

Gemini natively supports image, PDF, audio and video input:

{
  "contents": [{
    "role": "user",
    "parts": [
      {"text": "Summarize this image"},
      {"inline_data": {"mime_type": "image/png", "data": "<base64>"}}
    ]
  }]
}

Function calling

Full support for Gemini function_declarations and function_call; when accessed via the OpenAI-compatible endpoint, these are automatically converted to tools / tool_calls structures. See Function calling.

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Gemini 3 series (current mainline)

Gemini 2.5 series (stable)

Thinking and reasoning effort

Via parameter

Via model ID suffix

Multimodal generation

Embedding models

Examples

OpenAI-compatible protocol

Gemini native protocol

google-genai SDK

Vision and multimodal input

Function calling

API docs

​Gemini 3 series (current mainline)

​Gemini 2.5 series (stable)

​Thinking and reasoning effort

​Via parameter

​Via model ID suffix

​Multimodal generation

​Embedding models

​Examples

​OpenAI-compatible protocol

​Gemini native protocol

​google-genai SDK

​Vision and multimodal input

​Function calling

​API docs

Gemini 3 series (current mainline)

Gemini 2.5 series (stable)

Thinking and reasoning effort

Via parameter

Via model ID suffix

Multimodal generation

Embedding models

Examples

OpenAI-compatible protocol

Gemini native protocol

google-genai SDK

Vision and multimodal input

Function calling

API docs