White Blood Cell Classifier (ViT) vs Flux Dev: Which AI Model Should You Choose?
Pricing, context windows, latency, capabilities, and a one-line code switch β everything you need to pick the right model.
White Blood Cell Classifier (ViT) and Flux Dev are closely matched on pricing and context. The right choice depends on your specific workload β see the table below for the full breakdown.
Side-by-side specs
| Spec | White Blood Cell Classifier (ViT) | Flux Dev |
|---|---|---|
| Provider | huggingface | Replicate |
| Category | Image Generation | Image Generation |
| Input cost / 1M tokens | n/a | n/a |
| Output cost / 1M tokens | n/a | n/a |
| Context window | β | β |
| Max output tokens | β | β |
| Avg. latency | β | 10.0s |
| Featured | β | Yes |
| New | β | β |
| Capabilities | image | webp jpg png |
Pricing example
A typical chat workload of 100,000 input tokens plus 50,000 output tokens.
100K in Γ n/a + 50K out Γ n/a
100K in Γ n/a + 50K out Γ n/a
Switch in one line
Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/v1",
});
// Before β using White Blood Cell Classifier (ViT)
let r = await client.chat.completions.create({
model: "buketyeter/blood_cell_project",
messages: [{ role: "user", content: "Hello" }],
});
// After β switched to Flux Dev
r = await client.chat.completions.create({
model: "black-forest-labs/flux-dev-lora",
messages: [{ role: "user", content: "Hello" }],
});from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/v1",
)
# Before β using White Blood Cell Classifier (ViT)
r = client.chat.completions.create(
model="buketyeter/blood_cell_project",
messages=[{"role": "user", "content": "Hello"}],
)
# After β switched to Flux Dev
r = client.chat.completions.create(
model="black-forest-labs/flux-dev-lora",
messages=[{"role": "user", "content": "Hello"}],
)# Before β using White Blood Cell Classifier (ViT)
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "buketyeter/blood_cell_project",
"messages": [{"role": "user", "content": "Hello"}]
}'
# After β switched to Flux Dev
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "black-forest-labs/flux-dev-lora",
"messages": [{"role": "user", "content": "Hello"}]
}'Which one wins for...
Quick verdicts derived from public specs. Always validate on your own workload.
Higher coding category match or larger context wins.
Bigger context window helps maintain long-form coherence.
The larger context window is the deciding factor.
Multimodal/vision support is required for image inputs.
Lower average latency wins for interactive UX.
The model with the lower input-token price wins.
Frequently asked questions
Try White Blood Cell Classifier (ViT) and Flux Dev side by side
One API key, one endpoint, both models. Start free β no credit card required.