Analysis Summary
Kwaipilot KAT-Coder-Pro V2 is a benchmarked specialist model built primarily for software engineering. Its coding index and terminal-task result are excellent, and the 262K context window supports repository-level work. Tool use and function calling add practical value for development environments and scripted workflows.
It fits code generation, debugging, refactoring, test creation, and terminal-based engineering tasks where the problem is well defined. General intelligence is lower than flagship systems, and its agentic index is weak, so it should not independently manage broad business processes or complex research. Content quality is suitable for technical material but not its principal advantage.
Adopt it as a focused coding component rather than a general assistant. Its low pricing makes it attractive for frequent engineering calls, especially when paired with a stronger model for planning and review.
Assessed August 23, 2026
Editorial notes
KAT-Coder-Pro V2 is a cost-effective coding specialist with excellent programming performance, a 262K context, and strong terminal results, but limited general and agentic capability.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
KAT-Coder-Pro V2 is a cost-effective coding specialist with excellent programming performance, a 262K context, and strong terminal results, but limited general and agentic capability.
Benchmark scores
Magenta = intelligence · Ink = technical/agentic · Cyan = content & long-context · Grey = community benchmarks. Data: Artificial Analysis, Hugging Face.
33.7 Intelligence Index·59.5 Coding Index·15.1 Agentic Index
How Kwaipilot: KAT-Coder-Pro V2 compares
Kwaipilot: KAT-Coder-Pro V2 ranks #107 of 433 AI models we track for overall intelligence, #47 of 206 for coding, #109 of 188 for agentic tasks. Its 262K-token context window is larger than 72% of the models we list. At $0.30 per million input tokens it is cheaper than 48% of comparable models.
Dark bar = input · light bar = output, scaled to the priciest peer.
1M tokens ≈ 8 full-length novels or ~2,500 pages of business documents in a single request.
Strongest on business fit. The pulled-in intelligence corner is the trade-off, and if the shape matters more than the price, this is your model.
Compare shapes side-by-side →Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.30 | $0.000300 |
| Output | $1.20 | $0.001200 |
What would Kwaipilot: KAT-Coder-Pro V2 cost your business?
Pick the job that looks most like yours, then fine-tune with the sliders. Estimates update live.
A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 748 models → Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save Kwaipilot: KAT-Coder-Pro V2 for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout Kwaipilot: 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,..
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Frequently asked questions about Kwaipilot: KAT-Coder-Pro V2
How much does Kwaipilot: KAT-Coder-Pro V2 cost?
Kwaipilot: KAT-Coder-Pro V2 costs $0.30 per million input tokens and $1.20 per million output tokens.
What is the context window of Kwaipilot: KAT-Coder-Pro V2?
Kwaipilot: KAT-Coder-Pro V2 has a context window of 262,144 tokens (262K).
Is Kwaipilot: KAT-Coder-Pro V2 good for coding?
On our coding benchmark index, Kwaipilot: KAT-Coder-Pro V2 ranks #47 of 206 models, placing it in the top quartile of the field for code generation and debugging.
What can Kwaipilot: KAT-Coder-Pro V2 do?
Kwaipilot: KAT-Coder-Pro V2 supports tool use and function calling.
Who created Kwaipilot: KAT-Coder-Pro V2?
Kwaipilot: KAT-Coder-Pro V2 is developed by Kwaipilot and was released on March 27, 2026.
Data sourced from the OpenRouter API, Artificial Analysis, the Hugging Face Open LLM Leaderboard and our own internal testing. Scores are editorially curated by our team.
Last updated: September 3, 2026 8:38 pm