Zumik
Sign inSign up

LongCat-Flash-Chat

LongCat-Flash-Chat on Zumik: live pricing, context, and caching, routable by id or alias through one OpenAI-compatible endpoint.

Call it through ZumikAbout Fireworks AI
—
Input / 1M tokens
—
Output / 1M tokens
—
Cache read
256K
Context window

At a glance.

ProviderFireworks AI
Familylongcat
Released2026-04
LicenseOpen weights
Context window256K tokens
Max output64K tokens
Modalitiestext
Tool callingYes
Reasoning modeNo
Cachingautomatic
Batch discountNo batch tier

What reuse looks like here.

Not yet profiled

Pricing, context, and capabilities for LongCat-Flash-Chat 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.

Same OpenAI client, this model.

python
from openai import OpenAI

client = OpenAI(base_url="https://api.zumik.ai/v1", api_key="zk_live_...")

r = client.responses.create(
    model="longcat-flash-chat",
    input="Draft a fix for the failing test.",
)
print(r.usage.input_tokens_cached)   # confirm reuse

LongCat-Flash-Chat, answered.

How much does LongCat-Flash-Chat cost?

LongCat-Flash-Chat 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 LongCat-Flash-Chat's context window?

LongCat-Flash-Chat supports a 256K-token context window with up to 64K output tokens.

Does LongCat-Flash-Chat support prompt caching?

Yes. Fireworks AI uses Automatic prompt caching (serverless and dedicated) caching. In the Zumik corpus, LongCat-Flash-Chat shows a median cache capture of 82% on agent workloads.

Run LongCat-Flash-Chat with reuse measured.

Point an OpenAI client at Zumik and see exactly how much of this model's input you are reusing.

Migration quickstartBack to catalog