Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5
Key Points
Chinese AI company Moonshot AI has released Kimi K3.1 Pro, which jumped to fourth place on the Artificial Analysis Intelligence Index with 55 points, just behind Claude Opus 5.5.
The model shows major gains in agentic tasks, coding, and 3D modeling.
The parameter count stays at 2.8 trillion, but API prices drop by 33 percent. Moonshot plans to release the model weights by November 2026.
Moonshot AI's new reasoning model Kimi K3.1 Pro lands at number four on the Artificial Analysis Intelligence Index, just 3 points behind the top proprietary models.
Chinese AI company Moonshot AI has released Kimi K3.1 Pro , a post-trained version of its predecessor Kimi K3 with significantly more post-training compute. The model scores 55 points on the Artificial Analysis Intelligence Index , an 11-point jump over K3 that lands it in fourth place.
Only Claude Opus 5.5 (58), Claude Sonnet 5.5 (56), and Muse Wonder Full (56) rank higher, according to Artificial Analysis . If Moonshot releases the model weights as planned by November 4, 2026, K3.1 Pro would become the highest-scoring open-weights model available. That would shrink the gap between open and proprietary models from 12 to just 3 points, assuming nothing else changes by then. Ad
Alongside K3.1 Pro, Moonshot is shipping a variant called K3.1 Pro Highspeed that runs two to three times faster, according to the company. Both are available now through Kimi.ai , Kimi Work, Kimi Code, and the Kimi API . Ad
Agentic tasks see the biggest gains
K3.1 Pro's strongest improvements show up in agentic tasks. On the GDPval-AA benchmark, it reaches 1,846 Elo according to Artificial Analysis, 312 points above K3. That puts it just behind Claude Opus 5.5 (1,866) and Muse Wonder Full (1,857). On Terminal-Bench 4.0, Moonshot reports 65.7 percent, ahead of GPT-6 Astra (58.2 percent) and barely behind Claude Opus 5.5 (66.4 percent). K3 managed only 13 percent on the same test.
Hallucination rates dropped sharply too. On Moonshot's internal Kimi Hallu Bench Hard, claim-level hallucinations fell from 23.4 percent with K3 to 8.3 percent, according to the company. Moonshot says this is a stress test, not a measure of general error rates. GPT-6 Astra hits 5.5 percent on the same benchmark, so it still has an edge. Ad
On internal finance benchmarks, K3.1 Pro gained 7.2 points on Financial Modeling and 11.6 points on Financial Research. For general office tasks, the model tops all tested models on OfficeQA Pro v2, according to Moonshot.
Moonshot's Kimi K3.1 Pro benchmark table. All results with reasoning effort set to "max." Ad
3D modeling and coding
K3.1 Pro works directly with game engines like Unity, Unreal Engine, and Godot, handling both quick fixes and full project builds, according to Moonshot. On the BenchCAD benchmark, it scores 92.7 percent with tools, close to Claude Opus 5.5 (96.2 percent) and GPT-6 Astra (95.9 percent). Without tools, it hits 72.8 percent, nearly matching Claude Opus 5.5 (73.0 percent). Ad
Moonshot says the model is strongest at parametric modeling of mechanical parts, architectural models, and configurable products. It can write and run scripts for Blender and CAD software without MCP connectors. The company also launched a feature called "3D Make" on Kimi.com that lets users preview, edit, and export 3D models.
Same size, lower prices
K3.1 Pro keeps the same dimensions as K3, with 2.8 trillion total parameters and 104 billion active ones. The context window spans one million tokens, and the model handles both text and images. According to Artificial Analysis, it sits at the top of the Pareto frontier. No model with a comparable parameter count scores higher on the Intelligence Index.
Token prices drop by 33 percent. Input costs $2 per million tokens (down from $3), output costs $10 (down from $15), and cache hits cost $0.20 (down from $0.30). Each Intelligence Index task costs $1.98, almost identical to K3 ($2.00), since the model generates 14 percent more output tokens.
Cybersecurity and deliberate limits on offensive capabilities
Moonshot devotes an entire section of its technical blog post to cybersecurity. The company says it observed that vulnerability research capabilities kept growing as post-training scaled up, because cyber-related content was embedded in the general mid-training corpus and related tasks were part of the RL environment mix.
For the open-weights release, Moonshot stripped cyber-related data from both training phases. On its internal xdev-bench-hard, which reproduces recently disclosed vulnerabilities in software like the Linux kernel, Node.js, and JavaScriptCore, the uncleaned version solves 90.5 percent of tasks. After data cleaning, K3.1 Pro solves 81.0 percent. DeepSeek-V4.1-Flash reaches 85.7 percent on the same test, while Kimi K3 scored 33.3 percent.
Moonshot says no open-weights model should push the absolute frontier of offensive cyber capabilities. At the same time, the company wants to gradually raise what open models can do, since they also serve defensive purposes like auditing legacy systems. Anthropic previously showed that even GLM-5.3, which scores 45 on the AA Index, can write exploits at near-Claude Mythos levels .
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