How to Use the MiniMax M3.5 API for Batch Document Processing

MiniMax M3.5 works well as a batch worker: send one document per API call, force structured JSON output, and let a queue handle retries. The model is cheap enough on input tokens that processing a few hundred files stays in single-digit dollars, provided you do not re-send the same context on every retry.

If you only need chat-style use, the beginner walkthrough of MiniMax M3.5 covers it. This article is for builders.

How Do You Structure a Batch Job?

One job per document. Do not concatenate twenty files into one prompt to save calls — you lose the ability to retry a single failure and you waste tokens reprocessing good files.

• Read file, extract text.
• Send one API request with a schema-constrained prompt.
• Validate the JSON. On failure, retry with the specific error appended.
• Write the result and move on.

Retries that re-send the whole document are fine once. Loop five times and cost triples for one bad file.

How Do You Get Reliable JSON Output?

Ask for it explicitly and give a skeleton. “Return only JSON matching this shape: {\”vendor\”: string, \”amount\”: number, \”date\”: string}”. Then validate before trusting it. Models occasionally wrap JSON in markdown fences — strip those in code rather than re-prompting.

What Does Concurrency Cost You?

Rate limits, not money. Running ten requests in parallel finishes a batch fast but hits provider limits and triggers 429s. A modest pool — four to eight workers with retry-backoff — is the sweet spot for most document jobs.

Setting	Practical value
Concurrency	4-8 workers
Retry limit	3 with exponential backoff
Output	JSON, schema-validated

How Do You Keep Costs Down?

Two things. Strip boilerplate — headers, footers, page numbers — before sending. And cache results keyed by document hash so re-runs skip unchanged files. On a corpus of a few hundred contracts, deduplication and stripping alone can cut token spend significantly.

Where Does the Pipeline Break?

Scanned PDFs with no text layer. If your input is images, you need OCR before the model sees it, or you get confident nonsense. Also, documents in mixed languages can confuse extraction schemas — detect language and route separately if your corpus is multilingual.

Key Takeaways

• Process one document per API call, not concatenated batches.
• Force JSON output with an explicit schema and validate it.
• Use 4-8 concurrent workers with retry-backoff to respect rate limits.
• Strip boilerplate and cache by content hash to cut token spend.
• Add OCR before the model for scanned PDFs.