Llama 3.2 11B Vision Instruct

by Meta

All Meta releases

Llama 3.2 11B Vision Instruct is Meta's mid-size multimodal model, released September 2024. With 11 billion parameters and a 131,072-token context window, it accepts images alongside text and produces text, enabling image analysis, visual question answering, and grounded reasoning over documents and graphics. The model interprets layouts, charts, diagrams, and visual content within conversational workflows, and supports tool calling so downstream applications can act on visual insights programmatically. It fits document processing workflows, accessible content description, visual search systems, and tasks requiring scene understanding. Its open-weight design permits fine-tuning for domain-specific vision tasks, and the moderate parameter count keeps multimodal inference tractable.

Call Llama 3.2 11B Vision Instruct on Opper with the OpenAI SDK. Sign up without a credit card. Get started

Key info

Input
Output
Features
Context window
131K
Max output
131K
Input price
$0.24 /1M
Output price
$0.24 /1M
Released
  • US residency available
  • Zero data retention on pay-as-you-go
  • No training by default
  • GDPR DPA available

Available routes

Llama 3.2 11B Vision Instruct runs on 1 route through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

ProviderRegionZero data retentionTrainingInputOutputCache read
USNo$0.24$0.24Input price

Uptime and availability

Llama 3.2 11B Vision Instruct runs on a single route through the Opper gateway today, so its availability is that provider's availability.

100%route uptime, last 30 days
Single route, no failover target within this model.

Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. Set up a fallback chain.

Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider Opper monitors.

Data handling per route

Each route hosting Llama 3.2 11B Vision Instruct has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

DeepInfra United States🇺🇸

Zero data retention: nothing is logged, held for abuse monitoring or used for training. No training on customer data. US; unknown; DPA available.

Zero data retention
Yes. Nothing is logged, held for abuse monitoring or used for training.
Training
No training on customer data.
Logging
None
Abuse monitoring
No classifier
GDPR DPA
DPA available
Transfer mechanism
unknown

Benchmarks

Independent benchmark scores — composite indices for reasoning, coding, and math, plus individual eval scores where available.

Global rank#586 of 644 LLMs
TierEfficient
Output speed16 tok/s
First token0.60s
Intelligence Index5.4
Math Index1.7
Reasoning & knowledge
MMLU-Pro
46%
GPQA Diamond
22%
Humanity's Last Exam
6%
Long-context reasoning
13%
Coding
LiveCodeBench
11%
Agentic & tool use
Terminal-Bench Hard
1%
τ²-Bench Telecom
15%
Math & instruction following
AIME 2025
2%
IFBench
30%

Get started

Call Llama 3.2 11B Vision Instruct through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Llama 3.2 11B Vision Instruct is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.OPPER_API_KEY,
baseURL: "https://api.opper.ai/v3/compat",
});
const completion = await client.chat.completions.create({
model: "deepinfra/meta-llama/Llama-3.2-11B-Vision-Instruct",
messages: [{ role: "user", content: "Hello" }],
});
console.log(completion.choices[0].message.content);

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