Kimi K3.1 Pro benchmarks show strong coding and security capabilities

Moonshot AI has published comprehensive benchmark results for its new open-weight model, Kimi K3.1 Pro. The data, released in early November 2026, positions the model against major competitors like DeepSeek-V4.1-Flash-1017, GLM-5.3, and proprietary systems such as GPT-6 Astra and Claude Opus 5.5. This release provides engineers with concrete metrics to evaluate the model for local deployment or API integration.

What happened

The publication focuses on performance across coding, agentic workflows, reasoning, and cybersecurity domains. Kimi K3.1 Pro was evaluated using various harnesses, including Claude Code and Kimi Code, depending on the specific benchmark requirements. The results are presented alongside scores from other prominent models to offer a comparative landscape for developers assessing open-weight options.

In coding benchmarks, the model demonstrated competitive results. On Terminal-Bench 4.0, it achieved scores that place it among top-tier performers, though specific numerical values vary by evaluation method. For SWE-Marathon v1.1, the model was tested using the Claude Code harness, aligning its evaluation conditions with those of DeepSeek-V4.1-Flash-1017. Other models in this category were scored based on official leaderboard data.

The cybersecurity evaluations revealed notable strengths. In ExploitBench, Kimi K3.1 Pro reached a pass@5 maximum-capability score of 100, surpassing several comparison models. However, in ExploitGym, which allows a six-hour wall-clock budget per task, the model scored 42.4, indicating variability in performance depending on the time constraints and evaluation framework used.

How it works

Kimi K3.1 Pro supports configurable thinking effort through a reasoning_effort parameter in API requests. Developers can set this to "low", "high", or "max", with "max" being the default. This feature allows users to balance latency and computational cost against the depth of reasoning required for specific tasks.

For multi-turn conversations and tool use, the API requires strict message handling. The complete assistant message, including reasoning_content and tool_calls, must be passed back into the conversation history unchanged. This ensures the model maintains context and correctly interprets previous tool outputs and internal reasoning steps during extended interactions.

The model is designed to run on several inference engines, including vLLM, SGLang, and TokenSpeed. Moonshot AI provides recipes and cookbooks for these platforms to facilitate deployment. The API is accessible via the Kimi platform and offers compatibility with OpenAI and Anthropic standards, easing integration for existing applications.

Key details

• Kimi K3.1 Pro achieved a perfect 100 score on ExploitBench, outperforming many competitors in this specific cybersecurity metric.
• The model uses a reasoning_effort field with options for "low", "high", and "max" to control computational intensity.
• Evaluation harnesses varied by benchmark, with Claude Code used for Terminal-Bench and Kimi Code for ExploitGym.
• Internal benchmarks like Kimi Hallu Bench showed a hallucination rate of 5.4, which is lower than several compared models.
• The model supports OpenAI and Anthropic-compatible API formats for easier integration into existing workflows.
• Deployment recommendations include vLLM, SGLang, and TokenSpeed, with specific configuration guides provided for each.

Why it matters

For software engineers and technical leads, these benchmarks provide a data-driven basis for selecting models for code generation and security auditing tasks. The high score in ExploitBench suggests potential utility in automated vulnerability discovery, although the lower score in ExploitGym highlights the importance of evaluating models under different time and resource constraints. Understanding these nuances helps teams avoid overestimating capabilities in production environments.

The availability of open-weight models with detailed performance profiles allows organizations to consider local deployment options more seriously. This can reduce dependency on proprietary APIs and potentially lower costs for high-volume applications. However, the requirement for specific message handling in multi-turn conversations means developers must carefully implement API integration to avoid context loss or errors.

What you can do

• Review the specific benchmark scores relevant to your use case, such as coding or cybersecurity, before selecting a model.
• Test the reasoning_effort parameter in your application to find the optimal balance between speed and accuracy for your tasks.
• Ensure your API integration correctly passes back the full assistant message, including reasoning and tool call data, in multi-turn conversations.
• Evaluate the model using the recommended inference engines like vLLM or SGLang to ensure stable performance in your infrastructure.
• Compare the Kimi K3.1 Pro results with other models you are considering, paying attention to the evaluation harnesses used for fairness.
• Consider running internal tests on your specific workloads to validate the benchmark results in your unique environment.