Llama 3.2 11B Vision Instruct APIOPEN WEIGHTS131K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Llama 3.2 11B Vision Instruct?

Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...

Llama 3.2 11B Vision Instruct on the release timeline

Llama 3.2 11B Vision Instruct pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Llama 3.2 11B Vision Instruct is cheaper than 83% of the 309 models in the catalog.

What Llama 3.2 11B Vision Instruct costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Llama 3.2 11B Vision Instruct vs alternatives

When not to use Llama 3.2 11B Vision Instruct

• It is served by a single provider, so there is less failover headroom during an outage.

Specs

How to call Llama 3.2 11B Vision Instruct

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="meta-llama/llama-3.2-11b-vision-instruct",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/llama-3.2-11b-vision-instruct","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "meta-llama/llama-3.2-11b-vision-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Llama 3.2 11B Vision Instruct and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Llama 3.2 11B Vision Instruct

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Llama 3.2 11B Vision Instruct follows concrete instructions better than abstract ones.
• It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from Llama 3.2 11B Vision Instruct's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Llama 3.2 11B Vision Instruct

Popular open-source projects for running and building with Llama 3.2 11B Vision Instruct — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Llama 3.2 11B Vision Instruct

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → meta-llama/llama-3.2-11b-vision-instruct. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Llama 3.2 11B Vision Instruct ships open weights (meta-llama/Llama-3.2-11B-Vision-Instruct, ~56.1K downloads and 1.6K likes in the last 30 days), with community quantizations (fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 1 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff. See the error-code guide and failover setup.

Related reading

Frequently asked questions