voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options. Learn more about voyage-4-lite here: blog.voyageai.com/2026/01/15/voyage-4Opens in new tab
Modalities
Price
$0.02/M tokens
Context
32K
Released
Jul 27, 2026
This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Token volume and request traffic to this model over time.
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
| $0.02 | 0.10s |
Latency
0.10s
P50, best provider
100.00%
99.99%
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.
voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options. Learn more about voyage-4-lite here: blog.voyageai.com/2026/01/15/voyage-4
voyage-4-lite costs $0.02/M tokens.
voyage-4-lite accepts up to 32,000 tokens per input. Longer text has to be split into multiple inputs.
voyage-4-lite accepts text as input and returns embedding vectors.
voyage-code-4, voyage-multimodal-3.5, voyage-4 and 1 more are other embedding models from VoyageAI by MongoDB.
voyage-4-lite was released on July 27, 2026.