Meta Llama Llama 3.2 11B Vision Instruct

IBM watsonx chat

Meta Llama Llama 3.2 11B Vision Instruct is an IBM watsonx chat model.It supports a 128,000-token context windowwith up to 128,000 output tokens.Input is priced at $0.350/M tokens and output at $0.350/M tokens. Capabilities include function calling, vision. Route Meta Llama Llama 3.2 11B Vision Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Meta Llama Llama 3.2 11B Vision Instruct spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
0128,000
5,000
01,000,000
Per request
$0.001190
in $0.001050 · out $0.000140
Per day
$5.95
5,000 requests
Per month
$181
152,188 requests

Estimate uses $0.3500/M input · $0.3500/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input $0.350/M
Output $0.350/M

Limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
128,000 tokens
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls ✓ supported
  • Vision input ✓ supported
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Where it's strong

  • +parallel tool calls — only 21% of chat models on Future AGI advertise this

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Meta Llama Llama 3.2 11B Vision Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Meta Llama Llama 3.2 11B Vision Instruct via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Meta Llama Llama 3.2 11B Vision Instruct via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="watsonx/meta-llama-llama-3-2-11b-vision-instruct",
    messages=[{"role": "user", "content": "Hello, Meta Llama Llama 3.2 11B Vision Instruct!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="watsonx/meta-llama-llama-3-2-11b-vision-instruct",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Same model on other providers

meta-llama-llama-3-2-11b-vision-instruct is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
DeepInfra$0.0490/M$0.0490/MMay 12, 2026

Compare with similar models

Meta Llama Llama 3.2 11B Vision Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Meta Llama Llama 3.2 11B Vision Instruct cost?

Input is priced at $0.350 per 1M tokens and output at $0.350 per 1M tokens (IBM watsonx, last verified May 12, 2026).

What is the context window of Meta Llama Llama 3.2 11B Vision Instruct?

Meta Llama Llama 3.2 11B Vision Instruct supports a 128,000-token context window with up to 128,000 output tokens.

Does Meta Llama Llama 3.2 11B Vision Instruct support function calling?

Yes — Meta Llama Llama 3.2 11B Vision Instruct supports function (tool) calling, including parallel tool calls.

Is Meta Llama Llama 3.2 11B Vision Instruct good for production?

Meta Llama Llama 3.2 11B Vision Instruct is well-suited for parallel tool calls — only 21% of chat models on Future AGI advertise this. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Meta Llama Llama 3.2 11B Vision Instruct with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Meta Llama Llama 3.2 11B Vision Instruct

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.