Kimi K3.1 Pro and K3.1 Pro Highspeed Release – 3‑T‑Class Open Model with 1M‑Token Context and Faster Inference

TL;DR – What Kimi K3.1 Pro is and why it matters

Kimi launched K3.1 Pro, a 3‑trillion‑parameter open‑weight model with a 1 million‑token context window, and a Highspeed variant that runs 2–3× faster. The model matches or exceeds the strongest proprietary systems on coding, 3D creation, finance, and agentic benchmarks, while the team deliberately removed cyber‑related training data to keep offensive security capability below the frontier.

Model Overview and Availability

Key facts

Model size: 3 T parameters (post‑trained version of K3 with substantially larger RL compute).
Context window: 1 M tokens, the longest publicly announced for an open‑weight LLM.
Highspeed variant: 2–3× inference speed, same weights, optimized serving pipeline.
Release date: Available today on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Full model weights will be released on Nov 4 2026.
Pricing: $2.00 per MTok (prompt) and $10.00 per MTok (completion) for K3.1 Pro; cached usage $0.20/$2.00 per MTok.
Enterprise: K3.1 Pro is off by default for enterprise admins; can be enabled via Kimi Business.

Benchmark Performance – Coding

Conclusion: K3.1 Pro delivers frontier‑level coding ability, beating most open models and approaching top proprietary systems.

Terminal‑Bench 4.0: 65.7% pass rate, ahead of GPT‑6 Astra (58.2%) and just behind Claude Opus 5.5 (66.4%).
Kimi Code Bench 3.0 Long‑horizon: 61.2% vs K3’s 55.95%.
Human‑atlas simulation test: Built a full human walking dynamics simulator, iteratively fixed 36 shoulder‑muscle errors, and achieved stable 11.2 s walking after 167 missions and 420 reviews.
Community feedback: Users report that K3.1 Pro feels “spiky” in different areas compared to Astra and Opus, offering unique problem‑solving angles.

3D Modeling and Design – New Capabilities

Conclusion: The model excels at parametric 3D creation and CAD code generation, rivaling top proprietary models.

BenchCAD (with tools): 92.7% Voxel‑IoU, close to Claude Opus 5.5 (96.2%) and GPT‑6 Astra (95.9%).
Parametric modeling: Generates Blender and CAD scripts for mechanical parts, architecture, and custom furniture without needing MCP connectors.
3D Make: New web interface for turning prompts into printable 3D models, videos, or Live Photos.
User comments: Positive remarks on front‑end design quality and the model’s “blunt and argumentative” personality that some find entertaining.

Finance Benchmarks – End‑to‑End Workflows

Conclusion: K3.1 Pro sets a new open‑weight state‑of‑the‑art in financial analysis, achieving the highest OfficeQA Pro v2 score among open models.

OfficeQA Pro v2: 64.4, leading the leaderboard.
Frontier Finance: 58.6, outperforming most open competitors.
Internal finance gains: +7.2 points on Financial Modeling, +11.6 on Financial Research, and ~4.2× on Business Operation tasks.
Factual accuracy: Claim‑level hallucination rate dropped from 23.4% (K3) to 8.3%; overall factual error share fell to 42.4% of K3’s rate on the Kimi Hallu Bench Hard.

Cybersecurity – Controlled Capability Reduction

Conclusion: Kimi deliberately reduced offensive cyber ability by removing cyber‑related data, keeping the open‑weight model safer while preserving general coding strength.

Data removal: Cyber‑related content stripped from both mid‑training and RL stages.
xdev‑bench‑hard (pass@3): K3.1 Pro scores 81.0% vs 90.5% for an unreduced checkpoint; still ahead of DeepSeek‑V4.1‑Flash (85.7%) and far above K3 (33.3%).
Policy rationale: Open weights should not extend the absolute frontier of cyber capability; the team plans to raise capability gradually as safety controls mature.
Community reaction: Commenters note the approach is “a way better approach than refusal” and liken it to making offensive use economically unviable.

Agentic and Multimodal Benchmarks – Broad Skill Set

Conclusion: Across agentic, reasoning, and multimodal tasks, K3.1 Pro consistently matches or exceeds leading closed‑source models.

Agentic tasks: Strong results on BioMysteryBench, OfficeQA Pro, Frontier Finance, and OSWorld V2.1 (exact numbers omitted for brevity).
Multimodal: Competitive scores on MMMU‑Pro, GDP.pdf, and ZeroBench when evaluated with tool‑augmented pipelines.
Highspeed variant: Provides the same quality with 2–3× lower latency, useful for interactive agents.

Pricing, Quotas, and Practical Usage

Conclusion: The model is priced competitively but enforces a 5‑hour usage quota per session, which some early adopters find restrictive.

Quota issue: Users report hitting the 5‑hour limit after a single max‑effort session, making prolonged workflows difficult.
Cost comparison: At $2/MTok prompt cost, K3.1 Pro is cheaper than many US‑based alternatives; Chinese pricing is ~30 % lower than K3.
Performance vs cost: Benchmarks such as the Pac‑Man bake‑off show K3.1 Pro matching Claude Sonnet 5.5 quality at roughly half the price.

Community Sentiment and Open Questions

Conclusion: The Hacker News discussion highlights strong enthusiasm for the model’s capabilities, curiosity about the cyber‑safety approach, and concerns about quota limits.

"Great idea for handling their cyber, by nerfing the model specifically for cyber through removing cyber data from pretraining. This is a way better approach than refusal..." – @henry700

"The 5 h limit makes it completely unusable. You barely start doing anything and you hit the limit immediately." – @Athanase000

"Artificial Analysis index results show K3.1 Pro at rank 55, beating GPT‑6 Astra and Fable 5.1." – @minimaxir

"Highspeed is 2–3× faster, but is it a separate model?" – @vikramkr

Overall, the community views K3.1 Pro as a major step forward for open‑weight LLMs, especially in coding, 3D, and finance, while calling for clearer documentation on usage limits and the exact nature of the Highspeed variant.

Final Assessment

Kimi’s K3.1 Pro release demonstrates that open‑weight LLMs can now compete with the top proprietary systems on a wide range of technical tasks, thanks to massive RL compute, a 1 M‑token context, and a dedicated high‑speed serving path. The deliberate reduction of offensive cyber capability reflects a proactive safety stance, though the model’s current quota limits may hinder heavy‑use scenarios. The upcoming weight release on Nov 4 will enable broader research and downstream innovation.