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Kwaipilot: KAT-Coder-Pro V2.5

kwaipilot/kat-coder-pro-v2.5

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KAT-Coder-Pro V2.5 is a flagship-level Agentic Coding model that can directly hand over an entire issue or an entire business workflow to it, allowing it to autonomously locate and make modifications, and complete the entire process in the actual repository. At the same time, it seamlessly integrates multiple experts to fully retain the front-end aesthetic generation capability of V2.

Modalities
In / Out Price
$0.74 / $2.96per 1M
Context
262K
Released
Jul 10, 2026
Compare
ProvidersPricingPerformanceUptimeAppsActivityFAQExplore

Providers

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.

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$0.74$2.96$0.15----
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Not enough uptime data to display yet.

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.

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Frequently asked questions

KAT-Coder-Pro V2.5 is a flagship-level Agentic Coding model that can directly hand over an entire issue or an entire business workflow to it, allowing it to autonomously locate and make modifications, and complete the entire process in the actual repository. At the same time, it seamlessly integrates multiple experts to fully retain the front-end aesthetic generation capability of V2.

KAT-Coder-Pro V2.5 costs $0.74/M input tokens and $2.96/M output tokens, with separate rates for Cache Read at $0.15/M tokens.

KAT-Coder-Pro V2.5 has a 262,144 token context window. It supports up to 262,144 completion tokens.

Yes. KAT-Coder-Pro V2.5 accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

KAT-Coder-Pro V2.5 was released on July 10, 2026.

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KAT-Coder-Air V2.5 is a flagship-level Agentic Coding model that can directly hand over an entire issue or an entire business workflow to it, allowing it to autonomously locate and make modifications, and complete the entire process in the actual repository. At the same time, it seamlessly integrates multiple experts to fully retain the front-end aesthetic generation capability of V2.

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KAT-Coder-Pro V2

KAT-Coder-Pro V2 is the latest high-performance model in KwaiKAT’s KAT-Coder series, designed for complex enterprise-grade software engineering and SaaS integration. It builds on the agentic coding strengths of earlier versions, with a focus on large-scale production environments, multi-system coordination, and seamless integration across modern software stacks, while also supporting web aesthetics generation to produce production-grade landing pages and presentation decks.

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KAT-Coder-Pro V1

KAT-Coder-Pro V1 is KwaiKAT's most advanced agentic coding model in the KAT-Coder series. Designed specifically for agentic coding tasks, it excels in real-world software engineering scenarios, achieving 73.4% solve rate on the SWE-Bench Verified benchmark.

The model has been optimized for tool-use capability, multi-turn interaction, instruction following, generalization, and comprehensive capabilities through a multi-stage training process, including mid-training, supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and scalable agentic RL.

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