Meta Llama3.2 11B Instruct v1.0

Amazon Bedrock chat

Meta Llama3.2 11B Instruct v1.0 is an Amazon Bedrock chat model.It supports a 128,000-token context windowwith up to 4,096 output tokens.Input is priced at $0.350/M tokens and output at $0.350/M tokens. Capabilities include function calling, vision. Route Meta Llama3.2 11B Instruct v1.0 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 Llama3.2 11B Instruct v1.0 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
04,096
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
4,096 tokens
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • 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

  • +agentic workflows that depend on reliable tool calls
  • +document, chart, and screenshot understanding

Watch out for

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

Benchmarks pending

We haven't logged public benchmark scores for Meta Llama3.2 11B Instruct v1.0 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Meta Llama3.2 11B Instruct v1.0 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 Llama3.2 11B Instruct v1.0 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="bedrock/meta-llama3-2-11b-instruct-v1-0",
    messages=[{"role": "user", "content": "Hello, Meta Llama3.2 11B Instruct v1.0!"}],
)

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="bedrock/meta-llama3-2-11b-instruct-v1-0",
    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 ↗

Compare with similar models

Meta Llama3.2 11B Instruct v1.0 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 Llama3.2 11B Instruct v1.0 cost?

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

What is the context window of Meta Llama3.2 11B Instruct v1.0?

Meta Llama3.2 11B Instruct v1.0 supports a 128,000-token context window with up to 4,096 output tokens.

Does Meta Llama3.2 11B Instruct v1.0 support function calling?

Yes — Meta Llama3.2 11B Instruct v1.0 supports function (tool) calling.

Is Meta Llama3.2 11B Instruct v1.0 good for production?

Meta Llama3.2 11B Instruct v1.0 is well-suited for agentic workflows that depend on reliable tool calls and document, chart, and screenshot understanding. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Meta Llama3.2 11B Instruct v1.0 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 Llama3.2 11B Instruct v1.0

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