Artificial Intelligence
Moonshot AI releases Kimi K3.1 Pro
Moonshot AI released Kimi K3.1 Pro, which scored 55 on Artificial Analysis’s index, improved on several agentic and coding benchmarks, and came with lower API prices and a planned weights release.
- Moonshot AI released Kimi K3.1 Pro, a post-trained version of Kimi K3.
- Artificial Analysis scored it at 55 on the Intelligence Index, placing it fourth behind Claude Opus 5.5, Claude Sonnet 5.5 and Muse Wonder Full.
- Moonshot said it planned to release the model weights by 4 November 2026 and also shipped Kimi K3.1 Pro Highspeed.
- Moonshot said K3.1 Pro kept 2.8 trillion parameters and 104 billion active ones, while API prices fell by 33 per cent.
- Moonshot said it cleaned cyber-related data for the open-weights version, and on xdev-bench-hard the cleaned model scored 81.0 per cent versus 90.5 per cent for the uncleaned version.
Moonshot AI released Kimi K3.1 Pro, a post-trained follow-up to Kimi K3. Artificial Analysis gave it 55 points on its Intelligence Index, which put it fourth overall and 11 points above K3. The models ahead were Claude Opus 5.5, Claude Sonnet 5.5 and Muse Wonder Full.
Moonshot said it planned to publish the model weights by 4 November 2026. It said that, if that happened and other scores stayed unchanged, K3.1 Pro would become the highest-scoring open-weights model. The company also released Kimi K3.1 Pro Highspeed and said it ran two to three times faster.
Moonshot said the biggest gains appeared in agentic work. It reported 1,846 Elo on GDPval-AA and 65.7 per cent on Terminal-Bench 4.0, and said claim-level hallucinations fell from 23.4 per cent on K3 to 8.3 per cent on Kimi Hallu Bench Hard.
For coding and 3D tasks, Moonshot said the model worked with Unity, Unreal Engine and Godot, and could write and run scripts for Blender and CAD tools. It reported 92.7 per cent on BenchCAD with tools and 72.8 per cent without them. Moonshot also said K3.1 Pro kept 2.8 trillion parameters and 104 billion active ones, while prices for its API fell by 33 per cent.
Moonshot said it removed cyber-related data from both training stages for the open-weights version. It said xdev-bench-hard reproduced recent vulnerabilities in software such as the Linux kernel and Node.js, and that the cleaned model scored 81.0 per cent, down from 90.5 per cent for the uncleaned version. The company also cited Anthropic’s earlier point that GLM-5.3 could write exploits at near-Claude Mythos levels.
Named in this story
Companies
- Moonshot AI
- released Kimi K3.1 Pro and set its pricing
- Anthropic
- the company cited in Moonshot’s cyber discussion
Organisations
- Artificial Analysis
- ranked the models and published the index
Products and systems
- Kimi K3.1 Pro
- Moonshot’s new reasoning model
- Kimi K3
- the predecessor used for comparison
- Artificial Analysis Intelligence Index
- the ranking index used for the scores
- Claude Opus 5.5
- the top-ranked model above K3.1 Pro
- Claude Sonnet 5.5
- the second-ranked model above K3.1 Pro
- Muse Wonder Full
- another model ranked above K3.1 Pro
- GPT-6 Astra
- a rival model used in benchmark comparisons
- Kimi K3.1 Pro Highspeed
- the faster variant Moonshot shipped
- GDPval-AA
- a benchmark where Moonshot gave K3.1 Pro 1,846 Elo
- Terminal-Bench 4.0
- a benchmark where Moonshot reported 65.7 per cent
- Kimi Hallu Bench Hard
- Moonshot’s internal hallucination benchmark
- BenchCAD
- a 3D modelling benchmark used for tool and no-tool scores
- xdev-bench-hard
- Moonshot’s internal cyber benchmark
- DeepSeek-V4.1-Flash
- the model Moonshot compared on xdev-bench-hard
- GLM-5.3
- the model Anthropic cited in the cyber comparison
- Linux kernel
- one of the software targets on xdev-bench-hard
- Node.js
- one of the software targets on xdev-bench-hard
How the source tells it
The article was mainly a benchmark report, but it leaned into competitive framing and Moonshot’s own promotional claims about catching rival models.
- competitive hype the headline and repeated rank comparisons cast the model as closing in on rivals
- speculative framing a conditional future release was used to project a narrower open-versus-proprietary gap