Kimi K3.1 Pro Review: Moonshot AI Model Capabilities and API Costs

Kimi K3.1 Pro review starts with its October 23, 2026 launch by Moonshot AI as the initial model in the K3.1 series. The system builds on the prior K3 release through additional reinforcement learning across hundreds of domains. It targets coding projects, financial modeling, and agentic tasks with a 1,048,576-token context window.

Kimi K3.1 Pro Model Specifications and Capabilities

The model uses a Mixture-of-Experts design with 2.8 trillion total parameters and roughly 104 billion active parameters per token. It accepts text and image inputs, with video support listed in documentation. Users can set reasoning effort to low, high, or max, with the default at max for consistent output quality.

Performance metrics on vendor tests include 65.7 percent on Terminal-Bench 4.0, 51.7 percent on SWE-Marathon v1.1, and 61.2 percent on Kimi Code Bench 3.0 Long-horizon. BenchCAD with tools reached 92.7 percent. These scores place the model near Claude Opus 5.5 on some coding tasks while trailing on certain scientific terminal evaluations. Native support covers parallel tool calling, structured outputs, and streaming responses.

Automatic prompt caching applies with a default five-minute TTL and an optional one-hour setting. The flat per-token rate removes context-length pricing tiers that appear on competing platforms. Full open weights are scheduled for release by November 4, 2026.

Kimi K3.1 Pro Pricing Structure and Access Options

API rates stand at $2.00 per million input tokens, $10.00 per million output tokens, and $0.20 per million cached input tokens. Cache writes cost $2.00 for the five-minute option and $4.00 for the one-hour option. A highspeed variant doubles input and output rates while keeping cached input at the same level. Minimum top-up for API access equals one dollar.

Web and app access runs through membership tiers priced at roughly $15, $31, $79, and $159 per month on an annual basis. The $31 Pro tier includes highspeed access inside the applications. API usage bills separately from these memberships. Enterprise features remain off by default at launch.

Registration occurs at kimi.ai or through the OpenAI-compatible endpoint at api.moonshot.ai/v1. The interface supports 1M-token chats on higher tiers and provides agent credits that scale with membership level. No separate technical requirements beyond a standard browser or API client appear in the documentation.

Strengths, Limitations, and Market Position

Documented gains appear in long-horizon coding for Unity, Unreal, and Godot environments, parametric 3D modeling in Blender and CAD tools, and financial research workflows. Hallucination rates dropped on internal claim-level stress tests. Training adjustments reduced offensive cyber capabilities for the upcoming open release while preserving general coding utility.

Direct benchmark comparisons list Kimi K3.1 Pro at 65.7 percent against Claude Opus 5.5 at 66.4 percent and GPT-6 Astra at 58.2 percent on Terminal-Bench 4.0. On Agents’ Last Exam the score reaches 43.2 percent. Public user reviews remain limited because the model reached general availability only one day before these details were compiled.

Constraints include weaker results on some scientific terminal tasks and the absence of independent third-party audits at the time of launch. Enterprise controls require separate requests. The model remains below certain closed frontier systems on internal vulnerability benchmarks despite the post-training focus on coding domains.