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.

Which endpoint do I call for OCR 4.1?

POST https://api.llmapi.ai/v1/chat/completions with model "mistral-ocr-4-1" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to OCR 4.1 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does OCR 4.1 support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

OCR 4.1 API: Endpoint & Pricing

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.

Can you self-host OCR 4.1?

No. We found no public weights for OCR 4.1 on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API key See API code ↓

Providers

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

List price by provider

InputOutput

mistral$0 in

$0 out

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

Provider	Pricing	Context	Capabilities
mistral	in $0; out — per page	—	vision

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 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).

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to mistral-ocr-4-1 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-ocr-4-1",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_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)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "mistral-ocr-4-1",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="mistral-ocr-4-1",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "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."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID mistral-ocr-4-1 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Not supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

OCR 4.1 in production

Best workloadsLLM.API features

What the model is good at.

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.

What you get when you call OCR 4.1 through LLM.API.

Unified AI Routing
Reach OCR 4.1 and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale OCR 4.1.
More performance, less spend.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for OCR 4.1 alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Swap OCR 4.1 for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

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

LLM API rating for OCR 4.1

★ 2.5/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

2.5Production adoptionNot yet tracked — no public usage data for this release

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

2.1Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 2.5Coding 2.3Vision & multimodal 2.8

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about OCR models

LMArena—Speed—Sources: LMArena, Artificial Analysis

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.

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OCR 4.1 API: Endpoint & Pricing

What is OCR 4.1?

Can you self-host OCR 4.1?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

OCR 4.1 in production

Preprocessing for LLMs

Layout awareness

Searchable archives

Document text extraction

Data capture

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth