Chinese AI startup Moonshot AI has launched Kimi K3.1 Pro, its latest AI model featuring a one million token context window and improved capabilities across coding, 3D development, financial research and autonomous AI agents.
The company said Kimi K3.1 Pro scored 65.7% on Terminal Bench 4.0, a benchmark evaluating AI agents on real programming tasks, approaching Anthropic’s Claude Opus 5.5 at 66.4% and surpassing OpenAI’s GPT 6 Astra at 58.2%.
The model is built on Kimi K3 with additional training focused on improving complex reasoning and the ability to complete tasks requiring multiple steps and tools.
Moonshot also introduced Kimi K3.1 Pro Highspeed, a variant designed to deliver responses two to three times faster than the standard model.
Both versions are available through Kimi.ai, Kimi Work, Kimi Code and the company’s API, with full model weights scheduled for release by November 4.
Kimi K3.1 Pro improves long horizon coding capabilities
Moonshot reported that Kimi K3.1 Pro scored 61.2% on Kimi Code Bench 3.0 Long horizon, an internal benchmark measuring AI agents’ ability to complete complex programming tasks involving multiple steps, tool use and extended problem solving.
The result marks an improvement from the 55.95% recorded by its predecessor, Kimi K3.
The company also highlighted improvements in coding across game engines such as Unity, Unreal Engine and Godot, allowing the model to generate interactive environments, websites and 3D assets.
In one demonstration, Kimi K3.1 Pro developed a walking simulator using an anatomical model containing 296 skeletal components and 402 muscle components.
Across 167 development tasks and 420 reviews, the model refined the simulator until its virtual character could walk continuously for 11.2 seconds.
The model also demonstrated improved 3D modeling capabilities, scoring 92.7% on BenchCAD, compared with Claude Opus 5.5 at 96.2% and GPT 6 Astra at 95.9%.
Moonshot expands AI capabilities for financial research
Beyond coding, Moonshot is positioning Kimi K3.1 Pro for financial analysis, investment research and business operations.
The company reported a score of 64.4 on OfficeQA Pro v2, an evaluation measuring financial document analysis, information retrieval and numerical reasoning.
On its internal evaluations, Kimi K3.1 Pro improved by 7.2 points in financial modeling and 11.6 points in financial research compared with Kimi K3.
Moonshot also reported improvements in factual accuracy, with the model’s hallucination rate declining from 23.4% to 8.3% on its internal Kimi Hallu Bench Hard evaluation.
On the same evaluation using tools, Claude Opus 5.5 recorded a 15% hallucination rate, according to Moonshot’s published comparisons.
The company cautioned that the benchmark specifically tests difficult scenarios and does not represent hallucination rates during everyday use.
Moonshot plans open weights release in November
The launch comes as AI developers continue expanding access to advanced models that can be deployed independently of proprietary cloud platforms.
Moonshot said Kimi K3.1 Pro is its most capable model in the 3 trillion parameter class, with full weights expected to become publicly available on November 4.
The company also disclosed changes to its training process designed to reduce cybersecurity risks associated with releasing increasingly capable AI models.
Moonshot said it removed certain cybersecurity training data and tasks before evaluating the final model on an internal vulnerability research benchmark.
The resulting model scored 81% on the evaluation, compared with 90.5% for a version trained without those restrictions.
Moonshot said the changes were intended to limit offensive cybersecurity capabilities while preserving general coding performance.
Kimi K3.1 Pro is currently accessible through the company’s consumer, enterprise and developer platforms, with the planned model weights release expected to allow developers to run the model on their own infrastructure.
Disclosure: This article was edited by Estefano Gomez. For more information on how we create and review content, see our Editorial Policy.