Batch Inference: Why, When, and Cost Savings
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Update November 2, 2026 — Models. Mistral Large 3 (
mistral-large-2512) and Mistral Medium 3.5 were deprecated on October 31, 2026, and will be retired on April 30, 2027. Their designated successor, Mistral Large 4 (mistral-large-4), has been in public preview since October 6. The examples in this course use the aliasmistral-large-latest, which currently points to Mistral Large 3 according to its official datasheet (verified November 3, 2026). Similarly,mistral-medium-latestpoints to Mistral Medium 3.5. These aliases will remain valid until April 30, 2027. For new projects, testmistral-large-4, their designated successor.
Understanding Batch Inference
Once you move a language model to production, you face a trade-off: you need to process massive volumes of requests without letting costs spiral out of control. Batch inference solves exactly this problem. Instead of calling the API request by request, you group your demands and execute them in parallel at a reduced rate compared to individual calls.
The workflow follows a standard asynchronous pattern: you gather your requests into a file, submit the batch, move on to other tasks, and retrieve the results once processing finishes. This accepted latency is precisely what creates value: it gives Mistral AI the freedom to schedule work optimally across its GPU resources, and the resulting efficiency gains are passed on to you as a price reduction.
When to Use Batch Inference?
The decision criterion boils down to one question: is individual response latency critical? If no one is waiting in front of a screen, batch is the way to go. This applies to processing large corpora: a legal service that needs to classify and summarize forty thousand archived contracts has no need for a two-second response; it needs all forty thousand processed before Monday at a sustainable cost.
Mass content generation follows the same logic. An e-commerce catalog with sixty thousand items requiring product descriptions, or a campaign of personalized emails prepared the day before, are perfect candidates for overnight submission. Evaluation and benchmarking also fit here: when you measure a model’s performance on a test dataset, each request is independent of the others, and you only read the verdict once the entire set has run.
ETL pipelines and data enrichment form a third natural territory. You take an existing database and add AI-extracted metadata—sentiment, categorization, named entities—and the processing fits into an already asynchronous chain. Finally, large-scale moderation, where you must sift through millions of user-generated contents to detect violations, derives most of its economic benefit from this submission mode.
Conversely, if your application must respond while the user is watching—chatbot, interactive assistant, auto-completion—batch inference is off the table. These use cases belong to the standard synchronous API, and trying to force them into batch mode only produces frustration.
Compatible Endpoints
Batch inference supports eight Mistral API endpoints:
/v1/chat/completions— Conversational text generation/v1/embeddings— Semantic vector calculation/v1/fim/completions— Code completion (fill-in-the-middle)/v1/moderations— Content moderation/v1/ocr— Optical character recognition/v1/classifications— Text classification/v1/conversations— Multi-turn conversations/v1/audio/transcriptions— Audio transcription
This coverage changes how you design your mass processing tasks: semantic indexing of a document base, digitization of paper archives, and transcription of a fund of telephone recordings all go through the same unified mechanism, with the same submission and retrieval mechanics.
Two Submission Modes
Mistral offers two approaches for submitting your batch requests, and the choice essentially depends on volume. File batching scales up to one million requests: you prepare a JSONL file, upload it to the platform, and then create a job pointing to that file. This is the recommended method as soon as volumes become serious, especially because the file remains reusable and traceable.
Inline batching caps at ten thousand requests and eliminates the file step: you pass the requests directly in the API call body. For a script that generates its demands on the fly and has no reason to write an intermediate to disk, this is significantly faster to set up. We will detail each of these approaches in the following lessons.
Concrete Savings
Batch inference pricing differs from the standard API. On typical production volumes, starting from one hundred thousand requests per day, savings reach 50% or more compared to individual synchronous calls—enough to make viable treatments that a real-time rate would simply make impossible to budget. An often underestimated operational comfort is added: results do not expire. You can re-download them at any time after processing finishes, which dispenses with the need to build a local copy as a precaution.
Key Takeaways
- Batch inference groups your requests for asynchronous processing at a reduced cost
- It supports eight different endpoints, covering almost all use cases
- Two modes exist: file batching (up to 1M requests) and inline batching (up to 10K)
- Results are persistent and downloadable with no time limit
- This mode is not suitable for applications requiring real-time responses