Meta Llama 3.2 3B Instruct

SambaNova chat

Meta Llama 3.2 3B Instruct is a SambaNova chat model.It supports a 4,096-token context windowwith up to 4,096 output tokens.Input is priced at $0.0800/M tokens and output at $0.160/M tokens. Route Meta Llama 3.2 3B 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 3.2 3B Instruct spend

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

~3K in / ~400 out · 5K req/day
3,000
08,000
400
04,096
5,000
01,000,000
Per request
$0.000304
in $0.000240 · out $0.000064
Per day
$1.52
5,000 requests
Per month
$46.27
152,188 requests

Estimate uses $0.0800/M input · $0.1600/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 $0.0800/M
Output $0.160/M

Limits

Context window
4,096 tokens
Max input
4,096 tokens
Max output
4,096 tokens
Modalities
text

Capabilities

  • Function calling — not advertised
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • 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

Watch out for

  • !high cost — input + output rates are in the top 93% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !limited context — 4,096-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
  • !small context (under 16K tokens)
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Meta Llama 3.2 3B Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Meta Llama 3.2 3B 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 3.2 3B 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="sambanova/meta-llama-3-2-3b-instruct",
    messages=[{"role": "user", "content": "Hello, Meta Llama 3.2 3B 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="sambanova/meta-llama-3-2-3b-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 ↗

Compare with similar models

Meta Llama 3.2 3B 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 3.2 3B Instruct cost?

Input is priced at $0.0800 per 1M tokens and output at $0.160 per 1M tokens (SambaNova, last verified May 12, 2026).

What is the context window of Meta Llama 3.2 3B Instruct?

Meta Llama 3.2 3B Instruct supports a 4,096-token context window with up to 4,096 output tokens.

Does Meta Llama 3.2 3B Instruct support function calling?

Meta Llama 3.2 3B 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 Meta Llama 3.2 3B Instruct good for production?

Meta Llama 3.2 3B Instruct is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.

How can I route to Meta Llama 3.2 3B 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 3.2 3B Instruct

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