For those who aren't aware, OpenAI has a very similar batch mode (50% discount if you wait up to 24 hours): https://platform.openai.com/docs/api-reference/batch

It's nice to see competition in this space. AI is getting cheaper and cheaper!

We used the previous version of this batch mode, which went through BigQuery. It didn't work well for us at the time because we were in development mode and we needed faster cycle time to iterate and learn. Sometimes the response would come back much faster than 24 hours, but sometimes not. There was no visibility offered into what response time you would get; just submit and wait.

You have to be pretty darn sure that your job is going to do exactly what you want to be able to wait 24 hours for a response. It's like going back to the punched-card era. If I could get even 1% of the batch in a quicker response and then the rest more slowly, that would have made a big difference.

It would be nice if OpenRouter supported batch mode too, sending a batch and letting OpenRouter find the best provider for the batch within given price and response time.

Hah, I've been wrestling with this ALL DAY. Another example of Phenomenal Cosmic Powers (AI) combined with itty bitty docs (typical of Google). The main endpoint ("https://generativelanguage.googleapis.com/v1beta/models/gemi...") doesn't even have actual REST documentation in the API. The Python API has 3 different versions of the same types. One of the main ones (`GenerateContentRequest`) isn't available in the newest path (`google.genai.types`) so you need to find it in an older version, but then you start getting version mismatch errors, and then pydantic errors, until you finally decide to just cross your fingers and submit raw JSON, only to get opaque API errors.

So, if anybody else is frustrated and not finding anything online about this, here are a few things I learned, specifically for structured output generation (which is a main use case for batching) - the individual request JSON should resolve to this:

```json { "request": { "contents": [ { "parts": [ { "text": "Give me the main output please" } ] } ], "system_instruction": { "parts": [ { "text": "You are a main output maker." } ] }, "generation_config": { "response_mime_type": "application/json", "response_json_schema": { "type": "object", "properties": { "output1": { "type": "string" }, "output2": { "type": "string" } }, "required": [ "output1", "output2" ] } } }, "metadata": { "key": "my_id" } } ```

To get actual structured output, don't just do `generation_config.response_schema`, you need to include the mime-type, and the key should be `response_json_schema`. Any other combination will either throw opaque errors or won't trigger Structured Output (and will contain the usual LLM intros "I'm happy to do this for you...").

So you upload a .jsonl file with the above JSON, and then you try to submit it for a batch job. If something is wrong with your file, you'll get a "400" and no other info. If something is wrong with the request submission you'll get a 400 with "Invalid JSON payload received. Unknown name \"file_name\" at 'batch.input_config.requests': Cannot find field."

I got the above error endless times when trying their exact sample code: ``` BATCH_INPUT_FILE='files/123456' # File ID curl https://generativelanguage.googleapis.com/v1beta/models/gemi... \ -X POST \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H "Content-Type:application/json" \ -d "{ 'batch': { 'display_name': 'my-batch-requests', 'input_config': { 'requests': { 'file_name': ${BATCH_INPUT_FILE} } } } }" ```

Finally got the job submission working via the python api (`file_batch_job = client.batches.create()`), but remember, if something is wrong with the file you're submitting, they won't tell you what, or how.

Is this an indication of the peak of the AI bubble ?

In a way this is saying that there are some GPUs just sitting around so they would rather get 50% than nothing for their use.

Is it possible to use batch mode with fine-tuned models?

Man googles offerings are so inconsistent, batch processing has been available on vertex for a while now, I dont really get why they have two different offering in vertex and gemini, both are equally inaccessible

I've been using OpenAI's batch API for some time, then replaced it with Mistral's batch API because it was cheaper (Mistral Small with $0.10 / $0.20 per million tokens was perfect for my use case). This makes me rethink my choice, e.g. Gemini 2.5 Flash-Lite seems to be a better model[0] with only a slight price increase.

[0] https://artificialanalysis.ai/leaderboards/models

I really hope it means that 2.5 models will be available for batching in Vertex, too. We had spent quite a bit of effort making it work with BigQuery, and it's really cool when it works. There's edge-case, though, where it doesn't work: in case the batch is also referring to cached prompt. We did report this a few months ago.

Batch Mode for the Gemini API feels like Google’s way of asking, “What if we made AI more affordable and slower, but at massive scale?” Now you can process 10,000 prompts like “Summarize each customer review in one line” for half the cost, provided you’re willing to wait until tomorrow for the results.

I've been using this with nothing notable to mention besides there seems to be a common bug where you receive an empty text response.

https://discuss.ai.google.dev/t/gemini-2-5-pro-with-empty-re...

I find OpenAI's new flex processing more attractive, as it has the same 50% discount, but allows to use the same API as regular chat mode, so you can still do stuff where Batch API won't work (e.g. evaluating agents), and in practice I found it to work well enough when paired with client-side request caching: https://platform.openai.com/docs/guides/flex-processing?api-...