Fireworks AI · none caching
GLM 5.1
Z.ai open-weights model with strong agentic coding behaviour and balanced input/output pricing that favours output-heavy generation.
Specifications
At a glance.
| Provider | Fireworks AI |
| Family | glm_moe_dsa |
| Released | — |
| License | Open weights |
| Context window | 203K tokens |
| Max output | — |
| Parameters | 744B |
| Modalities | text |
| Tool calling | Yes |
| Reasoning mode | Yes |
| Caching | none |
| Batch discount | No batch tier |
Measured by Zumik
What reuse looks like here.
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="glm-5p1", # or an alias like code.balanced
input="Draft a fix for the failing test.",
)
print(r.usage.input_tokens_cached) # confirm reuseCompare
How GLM 5.1 stacks up.
Similar models
Other options for these workloads.
Frequently asked
GLM 5.1, answered.
How much does GLM 5.1 cost?
GLM 5.1 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 GLM 5.1's context window?
GLM 5.1 supports a 203K-token context window.
Does GLM 5.1 support prompt caching?
Yes. Fireworks AI uses Automatic prompt caching (serverless and dedicated) caching. In the Zumik corpus, GLM 5.1 shows a median cache capture of 83% on agent workloads.
Which Zumik aliases route to GLM 5.1?
GLM 5.1 is a candidate for the code.balanced alias, selected when it wins under current routing policy.
Run GLM 5.1 with reuse measured.
Point an OpenAI client at Zumik and see exactly how much of this model's input you are reusing.