Batch API

The Batch API is for asynchronous, high-volume inference jobs where you do not need an answer immediately.

Workflow

1. Prepare a request bundle
2. Create a batch with POST /v1/batches
3. Poll GET /v1/batches/{batch_id} — results are included inline once complete

1 & 2. Create the batch

Pass your requests inline — no separate file upload required.

$
curl https://api.meshapi.ai/v1/batches \
>
  -H "Authorization: Bearer <YOUR_RSK_KEY>" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "requests": [
>
      {
>
        "custom_id": "doc-001",
>
        "body": {
>
          "model": "openai/gpt-5.4",
>
          "messages": [{ "role": "user", "content": "Summarize this note." }]
>
        }
>
      }
>
    ],
>
    "completion_window": "24h"
>
  }'

Request item fields

Each entry in requests supports:

The batch create call also accepts an optional metadata object (arbitrary key-value pairs) alongside completion_window.

Limits

• A batch may not mix models — all requests must target the same model, or the create call returns 400 mixed_models.
• You can have at most 10 batches in a non-terminal state at once; an eleventh returns 429 batch_limit_exceeded.

3. Poll and read results

Poll until status is a terminal value. When completed, the response includes a results array — no separate file download needed.

$
curl https://api.meshapi.ai/v1/batches/batch_xyz789 \
>
  -H "Authorization: Bearer <YOUR_RSK_KEY>"

1
{
2
  "id": "batch_xyz789",
3
  "status": "completed",
4
  "request_counts": { "total": 1, "completed": 1, "failed": 0 },
5
  "results": [
6
    {
7
      "custom_id": "doc-001",
8
      "response": {
9
        "status_code": 200,
10
        "body": {
11
          "choices": [{ "message": { "content": "The note says..." } }],
12
          "usage": { "prompt_tokens": 12, "completion_tokens": 40 }
13
        }
14
      },
15
      "error": null
16
    }
17
  ]
18
}

Common statuses:

• validating
• in_progress
• finalizing
• completed
• failed
• cancelling
• cancelled
• expired

Notes

• All requests in a batch must use the same model.
• Batch jobs are best for throughput, not low-latency interactive use.
• Use GET /v1/batches to list recent batches and POST /v1/batches/{batch_id}/cancel to cancel one.
• Results are matched by custom_id — the output order is not guaranteed.