Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

OCR 4.1

Up to 30%

Turn documents and images into text with OCR 4.1 on LLM.API—built for capture pipelines that feed search and LLMs.

Start Using API

What is OCR 4.1?

OCR 4.1 focuses on reading text from documents and images. OCR model by Mistral. Access it via LLM.API without standing up a separate OCR vendor stack.

Providers

LLM.API routes OCR 4.1 to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
mistral$0.004 in
$0.005 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
mistral30% offin $0.004; out — per page—vision

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test OCR 4.1 right here — free to start.

OCR 4.1
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call OCR 4.1 through the OpenAI-compatible API — OCR / document endpoint via LLM.API (see docs).

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="mistral-ocr-4-1",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "mistral-ocr-4-1",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Preprocessing for LLMs

    Produces clean text that chat models can summarize or extract from. Grounded in the model's ocr role rather than generic chat claims.

  • Layout awareness

    Handles multi-column and mixed layout documents better than naive OCR. Tuned to how teams typically call OCR 4.1.

  • Searchable archives

    Turns paper archives into indexable text for search. Grounded in the model's ocr role rather than generic chat claims.

  • Document text extraction

    Reads printed and scanned pages into machine-usable text.

  • Data capture

    Feeds RPA and back-office pipelines that digitize forms and invoices. Tuned to how teams typically call OCR 4.1.

6 Most Valuable Use Cases

  • Screenshot-to-text for support tooling with OCR 4.1
  • Contract archive text extraction
  • Historical archive indexing projects with OCR 4.1
  • ID and form field capture for onboarding
  • Scanned PDF to searchable text pipelines with OCR 4.1
  • Warehouse label and packing-slip reading

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run OCR 4.1 on LLM.API?

  • Unified AI Routing

    Reach OCR 4.1 and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale OCR 4.1.

  • Reliability Layer

    Production: Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for OCR 4.1 alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap OCR 4.1 for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You want OCR output that downstream LLMs can analyze (OCR 4.1)
  • You must turn images or PDFs into text at API scale (OCR 4.1)
  • Your workflow starts with scans, photos, or screenshots (OCR 4.1)

Avoid if...

  • You require human-certified legal transcription without review
  • You need creative image generation rather than text extraction
  • Your documents are already clean digital text

COMMUNITY

What developers say about OCR models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to OCR 4.1.

  • Document-intelligence reviews score Mistral OCR around 4.3/5 for extraction accuracy, structured output and broad language coverage.
  • Practitioners highlight bounding boxes, confidence scores and JSON-schema output as the features that make it pipeline-ready.
  • It is framed as a developer/enterprise tool rather than an end-user document app.

SOURCES

Frequently Asked Questions

  • What is the context length for OCR 4.1?

    Reported context for OCR 4.1 is See provider specs. Always verify the active provider row if multiple providers are listed.

  • What are limitations of OCR 4.1?

    Like other API models, OCR 4.1 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Can I use tools or structured outputs with OCR 4.1?

    Tooling support varies; for pure ocr models, prefer the modalities listed rather than assuming chat tools.

  • Does OCR 4.1 support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • Is OCR 4.1 a chat model?

    No—OCR 4.1 is categorized as a ocr model. Use the matching API surface rather than assuming chat completions.

  • Does OCR 4.1 understand documents semantically?

    OCR 4.1 focuses on text extraction. For deep semantic analysis, pass OCR output into a chat model on LLM.API.

  • How do I call OCR 4.1 via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "mistral-ocr-4-1" and your LLM.API key. See the code snippet on this page.

  • How is OCR 4.1 priced on LLM.API?

    Listed pricing metadata shows: $0.004/page; $0.005/annotated page. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • What modalities does OCR 4.1 support?

    OCR 4.1 accepts document, image and produces text according to its architecture metadata on LLM.API.

COMPARE

Competitive Models

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales