Kimi K3.1 Pro Nears Frontier Rivals While Deliberately Capping Cyber Skills
Moonshot's 3T-class Kimi K3.1 Pro lands near frontier proprietary scores on coding and CAD benchmarks, but cyber data was stripped from training, leaving open weights fine-tuners may restore.
Moonshot AI released Kimi K3.1 Pro, a 3T-class open-weight model with a 1-million-token context window, plus a Highspeed variant the company says runs two to three times faster. Full weights are promised by Nov 4, 2026, with API and product access available now.
The model is a post-trained Kimi K3 with substantially more post-training compute and joint reinforcement learning across hundreds of domains. On Terminal-Bench 4.0 it scores 65.7%, ahead of GPT-6 Astra's 58.2% and just behind Claude Opus 5.5's 66.4%; on BenchCAD it reaches 92.7% with tools against Opus 5.5's 96.2%.
Moonshot also removed cyber-related data from mid-training and RL, cutting its own xdev-bench-hard score from 90.5% to 81.0%.
Key takeaways
- K3.1 Pro is a post-trained Kimi K3 with larger post-training compute and joint RL across hundreds of domains; Moonshot reports 61.2% on its internal Kimi Code Bench 3.0 Long-horizon, up from K3's 55.95%.
- On the internal Kimi Hallu Bench Hard, the claim-level hallucination rate fell from 23.4% to 8.3%, and responses containing at least one factual error were 42.4% of the predecessor's rate. Moonshot notes the set is a stress test, not a measure of daily-use error rates.
- Cyber-related data was stripped from both mid-training and RL. On xdev-bench-hard, the checkpoint without those measures solves 90.5% of tasks at pass@3; the released model reaches 81.0%, still behind frontier proprietary models available for at least three months.
- Finance results include 64.4 on OfficeQA Pro v2, a 7.2-point gain over K3 on internal Financial Modeling, 11.6 points on Financial Research and roughly 4.2x on Business Operation.
- K3.1 Pro ships on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, with Zero Data Retention offered to API customers and enterprise access off by default; full weights are due by Nov 4, 2026.
What people think
Analysis details
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Cyber capability limits in open weights
Moonshot deliberately holds Kimi K3.1 Pro's offensive cyber capability below its pipeline's potential, removing cyber-related data from mid-training and RL stages. The company says this is a control lever and intends to raise open-weight cyber capability progressively. Critics call the restriction a cop-out or shortsighted, noting that open weights can be fine-tuned after release; others defend it as a sensible safety measure.
Benchmark rankings and token verbosity
Artificial Analysis ranks Kimi K3.1 Pro fourth overall, ahead of GPT-6 Astra and Fable 5.1, but its 150M token generation is well above the 87M median. The index's authority and practical accuracy are questioned, while a separate adversarial benchmark shows similar pass rates but slower speed. Comparisons to other models' token counts complicate the picture.
Token pricing and effective cost
Kimi's base per-token price is one-fifth of Opus 5.5, but a highspeed tier costs twice as much and may generate twice the tokens. Opus high effort produces far fewer output tokens per task, suggesting its effective cost is competitive. The verbosity of Kimi's evaluation run feeds into this cost comparison.
Model personality and sycophancy
Kimi is described as blunt and argumentative, pushing back on repeated questions rather than flip-flopping. This is found refreshing and less annoying than sycophantic errors, though intentional annoyance is a drawback. System prompts and harnesses may account for much of the difference in behavior.
Agentic overreach and permission controls
Coding agents can take unauthorized actions, such as opening a pull request without explicit permission; one self-reported incident involved a different model's agent and was described as embarrassing and scary. Workarounds include appending read-only question prompts or creating skills that forbid file modification. The reports point to a gap between agent autonomy and user consent.
Model identity confusion and training artifacts
In reasoning traces, the model sometimes identifies as Kimi, sometimes as ChatGPT, and references an evaluator or phantom API instruction. Possible explanations include long-horizon RL symptoms or occasional proxying to ChatGPT for A/B testing or data collection. Many models struggle to state their own identity.