Llamaguard 7B
Fireworks AI chatLlamaguard 7B is a Fireworks AI chat model.It supports a 4,096-token context windowwith up to 4,096 output tokens.Input is priced at $0.200/M tokens and output at $0.200/M tokens. Route Llamaguard 7B via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Llamaguard 7B spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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
- 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
Strengths & caveats — when to pick Llamaguard 7B
Data-driven from Llamaguard 7B's percentile within chat peers.
Where it's strong
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
- !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 Llamaguard 7B yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Llamaguard 7B 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.# Llamaguard 7B 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-llamaguard-7b",
messages=[{"role": "user", "content": "Hello, Llamaguard 7B!"}],
)
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-llamaguard-7b",
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 ↗Explore similar models
Llamaguard 7B 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.
- DeepSeek V4.1 FlashFireworks AI · $0.300/M in · $1.20/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash 1017Fireworks AI · $0.300/M in · $1.20/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash 1017 UsFireworks AI · $0.450/M in · $1.80/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash (router · US)Fireworks AI · $0.450/M in · $1.80/M out · 1,048,576 ctxView model →
FAQ
How much does Llamaguard 7B 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 Llamaguard 7B?
Llamaguard 7B supports a 4,096-token context window with up to 4,096 output tokens.
Does Llamaguard 7B support function calling?
Llamaguard 7B 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 Llamaguard 7B good for production?
Llamaguard 7B 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 Llamaguard 7B 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 Llamaguard 7B
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.