Fireworks AI · none caching
Llama Guard 7B
Llama Guard 7B on Zumik: live pricing, context, and caching, routable by id or alias through one OpenAI-compatible endpoint.
Specifications
At a glance.
| Provider | Fireworks AI |
| Family | llama |
| Released | — |
| License | Open weights |
| Context window | 4K tokens |
| Max output | — |
| Parameters | 7B |
| Modalities | text |
| Tool calling | No |
| Reasoning mode | No |
| Caching | none |
| Batch discount | No batch tier |
Measured by Zumik
What reuse looks like here.
Pricing, context, and capabilities for Llama Guard 7B are live, but it is outside the flagship set Zumik benchmarks in depth, so measured reuse, capture, and warm TTFT are not shown yet. Run a workload estimate or route it by id to start collecting traces.
Call it
Same OpenAI client, this model.
from openai import OpenAI
client = OpenAI(base_url="https://api.zumik.ai/v1", api_key="zk_live_...")
r = client.responses.create(
model="llamaguard-7b",
input="Draft a fix for the failing test.",
)
print(r.usage.input_tokens_cached) # confirm reuseFrequently asked
Llama Guard 7B, answered.
How much does Llama Guard 7B cost?
Llama Guard 7B is an open-weights model routed through Fireworks AI. It is priced on the host's serverless size tier rather than a single published per-token list price, so it shows "—" here until profiled.
What is Llama Guard 7B's context window?
Llama Guard 7B supports a 4K-token context window.
Does Llama Guard 7B support prompt caching?
Yes. Fireworks AI uses Automatic prompt caching (serverless and dedicated) caching. In the Zumik corpus, Llama Guard 7B shows a median cache capture of 77% on agent workloads.
Run Llama Guard 7B with reuse measured.
Point an OpenAI client at Zumik and see exactly how much of this model's input you are reusing.