Meta Llama Guard 3.8B
SambaNova chatMeta Llama Guard 3.8B is a SambaNova chat model.It supports a 16,384-token context windowwith up to 16,384 output tokens.Input is priced at $0.300/M tokens and output at $0.300/M tokens. Route Meta Llama Guard 3.8B via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama Guard 3.8B spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.3000/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 | $0.300/M | |
| Output | $0.300/M |
Limits
- Context window
- 16,384 tokens
- Max input
- 16,384 tokens
- Max output
- 16,384 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
- !limited context — 16,384-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
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Meta Llama Guard 3.8B yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama Guard 3.8B 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 Guard 3.8B 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-guard-3-8b",
messages=[{"role": "user", "content": "Hello, Meta Llama Guard 3.8B!"}],
)
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-guard-3-8b",
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 ↗Compare with similar models
Meta Llama Guard 3.8B 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 Guard 3.8B cost?
Input is priced at $0.300 per 1M tokens and output at $0.300 per 1M tokens (SambaNova, last verified May 12, 2026).
What is the context window of Meta Llama Guard 3.8B?
Meta Llama Guard 3.8B supports a 16,384-token context window with up to 16,384 output tokens.
Does Meta Llama Guard 3.8B support function calling?
Meta Llama Guard 3.8B 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 Guard 3.8B good for production?
Meta Llama Guard 3.8B 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 Guard 3.8B 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 Guard 3.8B
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.