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OCR-4-0

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OCR-4-0 provides API OCR on LLM.API so scans and screenshots become structured, usable text.

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What is OCR-4-0?

OCR-4-0 is an OCR-oriented model on LLM.API (`mistral-ocr-4-0`). OCR model by Mistral. It takes document, image and produces text so document workflows can digitize content before LLM analysis.

Providers

LLM.API routes OCR-4-0 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-4-0 right here — free to start.

OCR-4-0
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Suggestions for your first prompt

Code snippet

Call OCR-4-0 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-4-0",
    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-4-0",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Image OCR

    Extracts text from photos, screenshots, and camera captures. Reflects OCR positioning for this endpoint.

  • Layout awareness

    Handles multi-column and mixed layout documents better than naive OCR. Reflects OCR positioning for this endpoint.

  • Data capture

    Feeds RPA and back-office pipelines that digitize forms and invoices. Grounded in the model's ocr role rather than generic chat claims.

  • Document text extraction

    Reads printed and scanned pages into machine-usable text. Relevant for `mistral-ocr-4-0` workloads on LLM.API.

  • Annotation workflows

    Supports annotated-page pricing paths when available from the provider. Grounded in the model's ocr role rather than generic chat claims.

6 Most Valuable Use Cases

  • Historical archive indexing projects with OCR-4-0
  • ID and form field capture for onboarding
  • Preprocessing scans before LLM extraction with OCR-4-0
  • Screenshot-to-text for support tooling
  • Invoice and receipt digitization with OCR-4-0
  • 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-4-0 on LLM.API?

  • Unified AI Routing

    Reach OCR-4-0 and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale OCR-4-0.

  • Reliability Layer

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

  • Observability

    Trace prompts, tokens, and errors for OCR-4-0 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

    Production: Swap OCR-4-0 for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • Your workflow starts with scans, photos, or screenshots (OCR-4-0)
  • You want OCR output that downstream LLMs can analyze (OCR-4-0)
  • You must turn images or PDFs into text at API scale (OCR-4-0)

Avoid if...

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

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-4-0.

  • 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

  • Can I use tools or structured outputs with OCR-4-0?

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

  • Does OCR-4-0 understand documents semantically?

    OCR-4-0 focuses on text extraction. For deep semantic analysis, pass OCR output into a chat model on LLM.API.

  • What is OCR-4-0?

    OCR model by Mistral. On LLM.API it is addressed as `mistral-ocr-4-0`.

  • Is OCR-4-0 a chat model?

    No—OCR-4-0 is categorized as a ocr model. Use the matching API surface rather than assuming chat completions.

  • Where is the canonical page for OCR-4-0?

    https://llmapi.ai/models/mistralai-mistral-ocr-4-0/

  • What modalities does OCR-4-0 support?

    OCR-4-0 accepts document, image and produces text according to its architecture metadata on LLM.API.

  • How is OCR-4-0 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.

  • Which providers serve OCR-4-0?

    LLM.API currently lists: mistral. Availability can vary by region and account.

  • What is the context length for OCR-4-0?

    Reported context for OCR-4-0 is See provider specs. Always verify the active provider row if multiple providers are listed.

  • How do I call OCR-4-0 via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "mistral-ocr-4-0" and your LLM.API key. See the code snippet on this page.

  • When should I choose OCR-4-0?

    Your workflow starts with scans, photos, or screenshots — especially when you specifically need OCR-4-0.

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