LLaVA 1.6 Vicuna 13B vs Depth Anything v2: Which AI Model Should You Choose?
Pricing, context windows, latency, capabilities, and a one-line code switch โ everything you need to pick the right model.
LLaVA 1.6 Vicuna 13B and Depth Anything v2 are closely matched on pricing and context. The right choice depends on your specific workload โ see the table below for the full breakdown.
Side-by-side specs
| Spec | LLaVA 1.6 Vicuna 13B | Depth Anything v2 |
|---|---|---|
| Provider | Replicate | Replicate |
| Category | Multimodal | Multimodal |
| Input cost / 1M tokens | n/a | n/a |
| Output cost / 1M tokens | n/a | n/a |
| Context window | 4K tokens | โ |
| Max output tokens | 1,024 | โ |
| Avg. latency | โ | โ |
| Featured | โ | Yes |
| New | โ | โ |
| Capabilities | image text | image |
Pricing example
A typical chat workload of 100,000 input tokens plus 50,000 output tokens.
100K in ร n/a + 50K out ร n/a
100K in ร n/a + 50K out ร 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 โ using LLaVA 1.6 Vicuna 13B
let r = await client.chat.completions.create({
model: "yorickvp/llava-v1.6-vicuna-13b",
messages: [{ role: "user", content: "Hello" }],
});
// After โ switched to Depth Anything v2
r = await client.chat.completions.create({
model: "chenxwh/depth-anything-v2",
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 โ using LLaVA 1.6 Vicuna 13B
r = client.chat.completions.create(
model="yorickvp/llava-v1.6-vicuna-13b",
messages=[{"role": "user", "content": "Hello"}],
)
# After โ switched to Depth Anything v2
r = client.chat.completions.create(
model="chenxwh/depth-anything-v2",
messages=[{"role": "user", "content": "Hello"}],
)# Before โ using LLaVA 1.6 Vicuna 13B
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "yorickvp/llava-v1.6-vicuna-13b",
"messages": [{"role": "user", "content": "Hello"}]
}'
# After โ switched to Depth Anything v2
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "chenxwh/depth-anything-v2",
"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 LLaVA 1.6 Vicuna 13B and Depth Anything v2 side by side
One API key, one endpoint, both models. Start free โ no credit card required.