Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.
Pricing
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.
Performance
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
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.
Apps
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.
Activity
Token volume and request traffic to this model over time.
API
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.
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
Pareto 26.9 costs $2.50/M input tokens and $7.50/M output tokens, with separate rates for Cache Read at $0.25/M tokens.
Pareto 26.9 has a 262,144 token context window. It supports up to 131,072 completion tokens.
Yes. Pareto 26.9 accepts tools and tool_choice for function calling. It supports response_format for JSON output, without JSON-schema enforcement.
Pareto 26.9 accepts text and images as input and returns text.
