google/gemma-4-26b-a4b-itGemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...
Billing follows the provider's own reported cost rather than the catalogue price — cached input is cheaper, and reasoning tokens bill as output. Your usage page is the record of what you actually paid.
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curl https://macdecloud.com/v1/chat/completions \
-H "Authorization: Bearer dcld-sk-..." \
-H "Content-Type: application/json" \
-d '{"model":"google/gemma-4-26b-a4b-it","messages":[{"role":"user","content":"Hello"}]}'Add "stream": true for token-by-token output over standard SSE.
curl -N https://macdecloud.com/v1/chat/completions \
-H "Authorization: Bearer dcld-sk-..." \
-H "Content-Type: application/json" \
-d '{"model":"google/gemma-4-26b-a4b-it","stream":true,"messages":[{"role":"user","content":"hi"}]}'This is a reasoning model: it produces a thinking pass before answering, and those tokens are billed as output — often far more of them than the answer itself. Two consequences. Too small a max_tokens gets consumed during reasoning and returns finish_reason: "length" with a null content, which looks like a failure but is just an exhausted budget (2000+ is a safer floor). And cost runs well above a non-reasoning model at the same headline price.
This model supports tools. Replies may carry tool_calls; run them and append the results as role: "tool" messages, then call again.
{
"model": "google/gemma-4-26b-a4b-it",
"messages": [{ "role": "user", "content": "What's the weather in Paris?" }],
"tools": [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": { "city": { "type": "string" } },
"required": ["city"]
}
}
}]
}Supports response_format, so the model returns JSON matching a schema you give it instead of prose you have to parse.
{
"model": "google/gemma-4-26b-a4b-it",
"messages": [{ "role": "user", "content": "..." }],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "result",
"schema": {
"type": "object",
"properties": { "answer": { "type": "string" } },
"required": ["answer"]
}
}
}
}Make content an array mixing text and image_url parts. The image can be a public URL or a data: base64 payload.
{
"model": "google/gemma-4-26b-a4b-it",
"messages": [{
"role": "user",
"content": [
{ "type": "text", "text": "What is in this image?" },
{ "type": "image_url", "image_url": { "url": "https://example.com/photo.jpg" } }
]
}]
}This model is also tagged for video input. The exact message shape is defined by the upstream provider and deCloud passes it through unchanged, so follow the provider's docs for those parameters.