per 1M tokens
nvidia/llama-nemotron-embed-vl-1b-v2
deepinfra/nvidia/llama-nemotron-embed-vl-1b-v2
The llama-nemotron-embed-vl-1b-v2 is a high-performance multimodal embedding model designed to transform text queries and document images into dense vector representations for advanced retrieval systems. It excels at understanding complex visual content like charts, tables, and infographics.
Price
Providers
Each row is one seller. Prices are the published rate from that source. A dash means that source did not publish the amount. Sorted by a 3:1 blend of input and output.
| Provider | Tag | Quant | Input | Output | Context | Uptime | Status | Source |
|---|---|---|---|---|---|---|---|---|
| DeepInfra | — | — | $0.01/1M | Not published— | 10K | — | Listed | deepinfra |
Monthly bill
Token models use the token counts. Image, video, and audio models use units times the flat rate.
$0.01 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0.01/1M | Not published— | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | token | $0.01 | Not published— | Not published— |
| 2026-10-09 | token | $0.01 | Not published— | Not published— |
| 2026-10-10 | token | $0.01 | Not published— | Not published— |
| 2026-10-11 | token | $0.01 | Not published— | Not published— |
| 2026-10-12 | token | $0.01 | Not published— | Not published— |
| 2026-10-13 | token | $0.01 | Not published— | Not published— |
| 2026-10-14 | token | $0.01 | Not published— | Not published— |
| 2026-10-15 | token | $0.01 | Not published— | Not published— |
| 2026-10-16 | token | $0.01 | Not published— | Not published— |
| 2026-10-17 | token | $0.01 | Not published— | Not published— |
| 2026-10-18 | token | $0.01 | Not published— | Not published— |
| 2026-10-19 | token | $0.01 | Not published— | Not published— |
| 2026-10-20 | token | $0.01 | Not published— | Not published— |
| 2026-10-21 | token | $0.01 | Not published— | Not published— |
| 2026-10-22 | token | $0.01 | Not published— | Not published— |
| 2026-10-23 | token | $0.01 | Not published— | Not published— |
| 2026-10-24 | token | $0.01 | Not published— | Not published— |
| 2026-10-25 | token | $0.01 | Not published— | Not published— |
| 2026-10-26 | token | $0.01 | Not published— | Not published— |
| 2026-10-27 | token | $0.01 | Not published— | Not published— |
| 2026-10-28 | token | $0.01 | Not published— | Not published— |
| 2026-10-29 | token | $0.01 | Not published— | Not published— |
| 2026-10-30 | token | $0.01 | Not published— | Not published— |
Alternatives
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- BAAI/bge-m3-multi$0.01/1M · Compare
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Parameters (1)
embed
Call this model
OpenRouter id deepinfra/nvidia/llama-nemotron-embed-vl-1b-v2. InferenceList does not send the request.
curl
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"deepinfra/nvidia/llama-nemotron-embed-vl-1b-v2","messages":[{"role":"user","content":"Hello"}]}'
JavaScript
await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.OPENROUTER_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "deepinfra/nvidia/llama-nemotron-embed-vl-1b-v2",
messages: [{ role: "user", content: "Hello" }],
}),
});
Python
import os, json, urllib.request
req = urllib.request.Request(
"https://openrouter.ai/api/v1/chat/completions",
data=json.dumps({"model": "deepinfra/nvidia/llama-nemotron-embed-vl-1b-v2", "messages": [{"role": "user", "content": "Hello"}]}).encode(),
headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"], "Content-Type": "application/json"},
)
print(urllib.request.urlopen(req).read().decode())