On October 23, Moonshot AI launched its new flagship, Kimi K3.1 Pro. The interesting part: this is not a new base model. It is a heavily post-trained upgrade of July's Kimi K3 — same architecture and parameter count, but with substantially larger post-training compute: large-scale joint reinforcement learning across "hundreds of downstream domains," backed by an overhaul of the RL framework, long-context inference, training efficiency, sandbox infrastructure, and rollout scheduling.

Same skeleton, two speeds

The specs match K3: 2.8 trillion total parameters, about 104 billion active (MoE), a 1-million-token context window, with text and image input. A companion K3.1 Pro Highspeed runs about 2-3x faster at the same quality, with double the API price. One engineering detail stands out: the K3.1 series supports thinking mode only (low/high/max, default max), and turning thinking off returns a 400 — Moonshot has effectively written "must reason" into the product contract.

Two sets of benchmarks: vendor numbers vs independent tests

The official tech report puts Terminal-Bench 4.0 at 65.7% (Claude-Code harness), ahead of GPT-6 Astra at 58.2% and just below Claude Opus 5.5 at 66.4%. Independent evaluator Artificial Analysis measured 67% — the highest score of any model it has tested, where Kimi K3 scored just 13%. On the AA Intelligence Index v4.3.2, K3.1 Pro scores 55.1, 4th overall, up 11.5 points from K3's 43.6, still 2.5 points behind the leader Opus 5.5 (57.6).

Hallucination is the most informative comparison in this launch. Moonshot's internal stress test, Kimi Hallu Bench Hard, shows claim-level hallucination falling from 23.4% to 8.3%; AA-Omniscience independently measured a fall from 53% to 44%. The improvement is real; the magnitude depends on who is measuring. Moonshot itself notes the test is built from "deliberately crafted" error-prone examples and does not represent everyday error rates.

Pricing, open weights, and a security trade-off

API pricing is $2/$10 per million input/output tokens ($0.20 on cache hits), about a third below K3's $3/$15 across the board. But AA found K3.1 Pro uses 14% more output tokens per task, so cost per task is roughly flat against K3 — the cut reads more like a customer-acquisition move than a free 30% saving.

Full weights are promised by November 4. AA's projection: once the weights land, K3.1 Pro becomes the highest-scoring open-weights model, narrowing the open-vs-proprietary gap on the index from 12 points to 3. Moonshot's track record here is decent — K3 launched July 16, promised weights by July 27, and shipped them on the day.

The cybersecurity section is the most self-aware part of the launch. Moonshot acknowledged that offensive cyber capability grows naturally as post-training scales, and removed cyber-related data from both the mid-training corpus and the RL environment mix. On its internal xdev-bench-hard, the checkpoint without removal solves 90.5% of tasks at pass@3; the final K3.1 Pro reaches 81.0% (K3: 33.3%). Moonshot says the model's offensive capability remains behind frontier proprietary models that have been publicly available for at least three months — though no outside lab has verified these numbers yet.

So what

The path K3.1 Pro represents is clear: this round's decisive edge is not a bigger base model but harder post-training. Same architecture, a rebuilt RL pipeline — and the index still jumps 11 points. Three things to watch: whether the weights ship on time on November 4, whether the 8.3% hallucination figure holds up in third-party settings, and whether "removed attack data yet stronger at coding" can be independently reproduced.

References: Kimi K3.1 Pro tech blog; felloai independent review and pricing check