Fireworks AI · none caching
DeepSeek Coder 1.3B Base
DeepSeek Coder 1.3B Base 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 | 16K tokens |
| Max output | — |
| Parameters | 1B |
| Modalities | text |
| Tool calling | No |
| Reasoning mode | Yes |
| Caching | none |
| Batch discount | No batch tier |
Measured by Zumik
What reuse looks like here.
Pricing, context, and capabilities for DeepSeek Coder 1.3B Base 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="deepseek-coder-1b-base",
input="Draft a fix for the failing test.",
)
print(r.usage.input_tokens_cached) # confirm reuseFrequently asked
DeepSeek Coder 1.3B Base, answered.
How much does DeepSeek Coder 1.3B Base cost?
DeepSeek Coder 1.3B Base 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 DeepSeek Coder 1.3B Base's context window?
DeepSeek Coder 1.3B Base supports a 16K-token context window.
Does DeepSeek Coder 1.3B Base support prompt caching?
Yes. Fireworks AI uses Automatic prompt caching (serverless and dedicated) caching. In the Zumik corpus, DeepSeek Coder 1.3B Base shows a median cache capture of 75% on agent workloads.
Run DeepSeek Coder 1.3B Base with reuse measured.
Point an OpenAI client at Zumik and see exactly how much of this model's input you are reusing.