Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| Flux-2-Klein-9b black-forest-labs/flux-2-klein-9b | Chrono Edit Lora nvidia/chrono-edit-lora | |
|---|---|---|
| Provider | Black Forest Labs | NVIDIA |
| Family | FLUX | |
| Modality | image | image |
| Context window | — | — |
| Max output | — | — |
| Released | 2026-01-14 | — |
| Input price | $0.040 /img | $0.020 /img |
| Output price | — | — |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | — |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
FLUX.2 [klein] 9B is a 9 billion parameter model that can generate images from text descriptions and supports multi-reference editing capabilities.
LoRA endpoint for the Chrono Edit model.
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try Flux-2-Klein-9b
client.chat.completions.create(
model="black-forest-labs/flux-2-klein-9b",
messages=[{"role":"user","content":"hello"}],
)
# Try Chrono Edit Lora — same client, same key
client.chat.completions.create(
model="nvidia/chrono-edit-lora",
messages=[{"role":"user","content":"hello"}],
)