Meta Llama Llama 3.2 3B Instruct
Hyperbolic chatMeta Llama Llama 3.2 3B Instruct is a Hyperbolic chat model.It supports a 32,768-token context windowwith up to 32,768 output tokens.Input is priced at $0.120/M tokens and output at $0.300/M tokens. Capabilities include function calling. Route Meta Llama Llama 3.2 3B Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama Llama 3.2 3B Instruct spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1200/M input · $0.3000/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 | $0.120/M | |
| Output | $0.300/M |
Limits
- Context window
- 32,768 tokens
- Max input
- 32,768 tokens
- Max output
- 32,768 tokens
- Modalities
- text
Capabilities
- Function calling ✓ supported
- Parallel tool calls ✓ supported
- 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
- +parallel tool calls — only 21% of chat models on Future AGI advertise this
Watch out for
- !limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG
- !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 3B Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama 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.
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 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="hyperbolic/meta-llama-llama-3-2-3b-instruct",
messages=[{"role": "user", "content": "Hello, Meta Llama 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="hyperbolic/meta-llama-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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Same model on other providers
meta-llama-llama-3-2-3b-instruct is also available via 3 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| IBM watsonx | $0.150/M | $0.150/M | May 12, 2026 |
| Novita AI | $0.0300/M | $0.0500/M | May 12, 2026 |
| DeepInfra | $0.0200/M | $0.0200/M | May 12, 2026 |
FAQ
How much does Meta Llama Llama 3.2 3B Instruct cost?
Input is priced at $0.120 per 1M tokens and output at $0.300 per 1M tokens (Hyperbolic, last verified May 12, 2026).
What is the context window of Meta Llama Llama 3.2 3B Instruct?
Meta Llama Llama 3.2 3B Instruct supports a 32,768-token context window with up to 32,768 output tokens.
Does Meta Llama Llama 3.2 3B Instruct support function calling?
Yes — Meta Llama Llama 3.2 3B Instruct supports function (tool) calling, including parallel tool calls.
Is Meta Llama Llama 3.2 3B Instruct good for production?
Meta Llama Llama 3.2 3B Instruct is well-suited for parallel tool calls — only 21% of chat models on Future AGI advertise this. Consider alternatives if you need limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG.
How can I route to Meta Llama 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 Llama 3.2 3B Instruct
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.