Llama V3.2 11B Vision Instruct

Fireworks AI chat

Llama V3.2 11B Vision Instruct is a Fireworks AI chat model.It supports a 131,072-token context windowwith up to 16,384 output tokens.Input is priced at $0.200/M tokens and output at $0.200/M tokens. Capabilities include vision. Route Llama V3.2 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: Oct 22, 2026 View source ↗
Cost calculator

Estimate Llama V3.2 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
0131,072
400
016,384
5,000
01,000,000
Per request
$0.000680
in $0.000600 · out $0.000080
Per day
$3.40
5,000 requests
Per month
$103
152,188 requests

Estimate uses $0.2000/M input · $0.2000/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 Oct 22, 2026.

Input $0.200/M
Output $0.200/M
Blended (3:1 in:out) $0.200/M
Cross-model comparable price

Context & limits

Context window
131,072 tokens
Max input
131,072 tokens
Max output
16,384 tokens
Modalities
vision, text

Capabilities

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

Strengths & caveats — when to pick Llama V3.2 11B Vision Instruct

Data-driven from Llama V3.2 11B Vision Instruct's percentile within chat peers.

Where it's strong

  • +document, chart, and screenshot understanding

Watch out for

  • !high cost — input + output rates are in the top 84% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback

Benchmarks pending

We haven't logged public benchmark scores for Llama V3.2 11B Vision Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Llama V3.2 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.
# Llama V3.2 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="fireworks-ai/accounts-fireworks-models-llama-v3p2-11b-vision-instruct",
    messages=[{"role": "user", "content": "Hello, Llama V3.2 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="fireworks-ai/accounts-fireworks-models-llama-v3p2-11b-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 ↗

Explore similar models

Llama V3.2 11B Vision Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Llama V3.2 11B Vision Instruct cost?

Input is priced at $0.200 per 1M tokens and output at $0.200 per 1M tokens (Fireworks AI, last verified Oct 22, 2026).

What is the context window of Llama V3.2 11B Vision Instruct?

Llama V3.2 11B Vision Instruct supports a 131,072-token context window with up to 16,384 output tokens.

Does Llama V3.2 11B Vision Instruct support function calling?

Llama V3.2 11B Vision Instruct does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.

Is Llama V3.2 11B Vision Instruct good for production?

Llama V3.2 11B Vision Instruct is well-suited for document, chart, and screenshot understanding. Consider alternatives if you need high cost — input + output rates are in the top 84% of priced chat peers; consider a cheaper sibling for high-volume workloads.

How can I route to Llama V3.2 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 Llama V3.2 11B Vision Instruct

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