Kimi K3.1 Pro Hands-On: A Practical Operator Guide

Moonshot's new 3T-class open-weight flagship pairs a 1M-token context with thinking-only mode, a Highspeed variant and a quietly dangerous fallback rule. The official Kimi Code documentation says how to configure, route and budget it.

What Actually Landed

Moonshot AI introduced Kimi K3.1 Pro in late October 2026 as the first model of the new K3.1 series, describing it as its most capable 3T-class open-weight model to date with a one-million-token context window; the coverage carries a publication date of October 23, 2026 and points to a company blog entry dated October 24, 2026 (source: Unite.AI, October 23, 2026). A companion variant, Kimi K3.1 Pro Highspeed, delivers roughly two to three times the speed with identical model capabilities at about twice the quota consumption (source: Unite.AI, October 23, 2026).

Before the announcement, the trail was visible. On October 19 an SGLang commit titled "Kimi-K3.1 support for the K3 chat encoder" added K3.1 chat-format adaptation with 107 test cases, and the K3.1 tokenizer file was reported as byte-identical to K3 with the same 16 special tokens — an encoding-compatibility check rather than a performance run (source: IT之家, October 23, 2026). The same report notes that the K3.1 tokenizer work was said to add a medium effort tier on top of the low, high and max levels already offered by K3, with the default still max, while the official Kimi Code model table documents three levels (source: IT之家, October 23, 2026). That difference between pre-release code reading and shipped documentation is worth keeping in view rather than resolving silently.

The Six Model IDs, and How to Pick One

The Kimi Code documentation is the most operationally useful artefact to come out of this launch, because it publishes a full model table rather than a marketing page. Six model IDs are offered across four families: k3.1-pro, k3.1-pro-highspeed, k3, k3-256k, kimi-for-coding and kimi-for-coding-highspeed. K3.1 Pro is described as a 3T-class flagship with a context window of up to 1,048,576 tokens for higher-tier members; K3 is the previous-generation flagship at 2.8T parameters; k3-256k is the 256K-context cut of K3; kimi-for-coding maps to K2.8 Preview and kimi-for-coding-highspeed to K2.7 Code Highspeed, which is described as roughly five to six times faster at about three times the quota (source: Kimi Code documentation, Model Configuration).

Plan gating is explicit in the same table. Under the current membership plans, k3.1-pro requires Plus or above while the full one-million-token window is reserved for Pro and above; k3.1-pro-highspeed requires Pro and above. Under the legacy plans the equivalent tiers are Moderato, Allegretto and Andante (source: Kimi Code documentation, Model Configuration). Multimodal input is supported for image and video on K3.1 Pro, K3.1 Pro Highspeed and K3, while k3-256k accepts images only — a detail that breaks a switch if a session contains video files.

Thinking Is Not a Setting Here

The single most consequential configuration detail is that K3.1 Pro models support thinking mode only. Requests with thinking disabled are rejected with an HTTP 400 error, and the same applies when a third-party tool sends none, off or disabled (source: Kimi Code documentation, Model Configuration). By contrast, the K3 series, K2.8 Preview and K2.7 Code Highspeed are routed to K2.8 Preview without thinking when thinking is turned off, and K2.7 Code Highspeed does not support an adjustable effort level at all (source: Kimi Code documentation, Model Configuration).

For third-party clients the documentation publishes an explicit effort mapping. No effort value means the model default, which is max for K3.1 Pro and K2.8 Preview and high for the K3 series. Ultra, max and xhigh all resolve to max; high resolves to high; low resolves to low; any unrecognised value returns HTTP 400 (source: Kimi Code documentation, Model Configuration). The same table shows the reasoning_effort default per model: max for k3.1-pro, k3.1-pro-highspeed, k3-256k and kimi-for-coding-highspeed, and high for k3 and kimi-for-coding (source: Kimi Code documentation, Model Configuration).

The Mistyped-ID Failure Mode

The sharpest edge in the documentation is a silence. A mistyped model ID does not raise an error — it falls back to K2.8 Preview without thinking, silently (source: Kimi Code documentation, Model Configuration). The documentation's own troubleshooting section names the consequence: because the Highspeed ID must be exactly k3.1-pro-highspeed or kimi-for-coding-highspeed, a wrong value falls back to K2.8 Preview without thinking, which produces no speedup and no error (source: Kimi Code documentation, Model Configuration).

The second explanation for a missing speedup is more mundane and more common: the Highspeed variant only accelerates model output, while tool calls — reading and writing files, running commands — and script execution are unaffected. When those dominate a turn, the perceived gain shrinks (source: Kimi Code documentation, Model Configuration). Both failure modes share a symptom, which is why the documented advice is to paste the model ID from the table rather than typing a display name such as "K3.1 Pro" or "K3.1 Pro Highspeed".

Session Discipline and Cache Cost

Another documented rule has direct billing consequences. Switching models or changing thinking effort invalidates the existing context cache, which forces a fresh prefill and increases token usage; reasoning chains from different models can also affect one another. The documentation therefore recommends keeping the same model and the same effort within a session, and starting a new session rather than switching mid-conversation when upgrading from a K3-series, K2.8 Preview or K2.7 Code Highspeed session (source: Kimi Code documentation, Model Configuration). The upgrade path is explicitly a new session, not a dropdown switch.

For integrations, the documented base URLs split by region: https://api.kimi.com/coding/v1 for the China-hosted OpenAI-compatible Responses and Chat Completions endpoints, https://api.kimi.ai/coding/v1 for the overseas equivalent, and Anthropic-compatible endpoints at https://api.kimi.com/coding/ and https://api.kimi.ai/coding/ (source: Kimi Code documentation, Model Configuration). The recommendation to prefer the native Responses, chat completion or message APIs over protocol translation through proxy tools is worth noting for anyone layering a gateway in front of the model.

Reported Performance, and Its Limits

All benchmark figures below are vendor-reported, and no independent re-test was available at the time of writing. On Terminal-Bench 4.0, K3.1 Pro is reported at 65.7 percent, ahead of GPT-6 Astra at 58.2 percent and narrowly behind Claude Opus 5.5 at 66.4 percent. On the internal Kimi Code Bench 3.0 Long-horizon, it is reported at 61.2 percent against K3's 55.95 percent, and on BenchCAD at 92.7 percent with tools, against 96.2 percent for Opus 5.5 and 95.9 percent for GPT-6 Astra (source: Unite.AI, October 23, 2026).

Two caveats belong beside those numbers. First, the Kimi Hallu Bench Hard claim-level hallucination rate is reported as falling from 23.4 percent for K3 to 8.3 percent, with responses containing at least one factual error at 42.4 percent of the predecessor's rate — but the source itself notes the evaluation is deliberately built to probe error-prone situations and that the rates do not represent error rates in daily use (source: Unite.AI, October 23, 2026). Second, and more consequential for open-weight users, cyber-related data was removed from both mid-training and the reinforcement learning mix for the open-weight release: a checkpoint trained without those measures solves 90.5 percent of xdev-bench-hard problems at pass@3, while the final K3.1 Pro reaches 81.0 percent, which Moonshot presents as evidence that data removal meaningfully reduces cyber capability (source: Unite.AI, October 23, 2026).

The practical read for an operator is that the documentation is the reliable artefact, the benchmarks are directional, and the self-hosted path has a fixed calendar attached. Full model weights for Kimi K3.1 Pro are stated to be released by November 4, 2026 (source: Unite.AI, October 23, 2026). Until then, the API is the only route, and the model table is the only reliable way to know which model actually served a request.