Kimi K3.1 Pro: Pricing, Benchmarks, Open Weights Date

Kimi K3.1 Pro is Moonshot AI's new flagship, launched October 23, 2026 at $2 input / $10 output per million tokens, a third below Kimi K3, with full open weights promised by November 4. Moonshot reports 65.7% on Terminal-Bench 4.0, narrowly behind Claude Opus 5.5 at 66.4%. The catch for anyone who pays per token is that sticker price and cost per finished task are different numbers.

This post covers what it is, what it costs, and whether to switch.

Official Sources#

What Shipped#

Moonshot calls K3.1 Pro "a post-trained version of Kimi K3, with substantially larger post-training compute," trained with reinforcement learning across "hundreds of downstream domains." It is the first model in a K3.1 series and has a 1-million-token context window. A Highspeed variant delivers 2-3x the speed. Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API; Kimi Enterprise access is off by default.

Artificial Analysis lists the model at 2.8T total and 104B active parameters, text and image input, and weights not yet published. Moonshot has not said which license the weights will use. For K3 the license was a custom one, covered in our K3 open weights post, so read the terms before committing to a self-hosted rollout.

Benchmarks: Vendor Claims vs Independent#

All of the following from Moonshot are vendor-reported, run at max reasoning effort, and mostly in a Claude Code harness:

• Terminal-Bench 4.0: 65.7%, versus GPT-6 Astra 58.2% and Claude Opus 5.5 66.4%.
• Kimi Code Bench 3.0 Long-horizon (an internal benchmark): 61.2%, up from K3's 55.95%.
• OfficeQA Pro v2: 64.4, which Moonshot calls state of the art.
• BenchCAD with tools: 92.7%, against Opus 5.5 at 96.2% and GPT-6 Astra at 95.9%.
• Kimi Hallu Bench Hard (internal stress test): claim-level hallucination 8.3%, down from 23.4% for K3. Moonshot notes this is not an everyday error rate.

Artificial Analysis's independent numbers are more measured. It scores K3.1 Pro 55 on its Intelligence Index, fourth place behind Claude Opus 5.5 (58), Claude Sonnet 5.5 (56) and Muse Wonder Full (56), and 11 points above K3. It reports 67% on Terminal-Bench 4.0 for K3.1 Pro, the highest of any model it lists, ahead of Sonnet 5.5 at 64%. That is a big jump from 13% for K3 under its harness, which tells you the gain is real but also that harness choice moves these numbers a lot.

Pricing and the Cost-Per-Task Catch#

Artificial Analysis confirms the $2 / $10 / $0.20 rates, 33% below K3 on all three. Its cost-per-task figure is the number to hold on to: roughly $1.98 per Intelligence Index task, about equal to K3, because K3.1 Pro uses 55K output tokens per task, 14% more than K3's 48K. A cheaper token does not mean a cheaper task when the model thinks longer.

Third-party analysis points the same way. A MindStudio write-up of one independent six-task coding run, which we have not reproduced, reports about 112 million tokens in total, mostly cached input re-read at every agent step, for roughly $32. In that single run it says Haiku 5.5 scored higher for around a quarter of the price. Treat it as one data point, not a verdict, but it shows where the bill hides: long agentic loops re-read context constantly, so cache-hit price and step count matter as much as the headline rate.

What To Do Monday#

1. Change one string. If you call K3 through the Kimi API, the swap is kimi-k3 to kimi-k3.1-pro. Run your own five-task set and record pass rate, completion tokens including thinking tokens, and retries, not just pass rate.
2. Compare cost per task, not per token. Run the same tasks on DeepSeek V4.1 Flash and on whichever Claude or GPT model you use today, such as GPT-6.1 Terra at $1/$5. Pick the cheapest model that clears your own pass bar.
3. Keep Highspeed for interactive work only. It costs exactly double per token for the same model, per Moonshot's pricing as reported by AIHubMix. Use it where latency changes behavior, not in batch runs.
4. Wait on self-hosting. A 2.8T-parameter model needs a multi-accelerator cluster. For K3, one analysis put the 4-bit weights alone near 1.4 TB. Nothing about K3.1 Pro should change that, and the weights are not out until November 4.
5. Check the cyber caveat. Moonshot says it removed cyber-related data from mid-training and the RL mix for the open release. On its internal xdev-bench-hard the final model solves 81.0% at pass@3 against 90.5% for a checkpoint without those controls. If you do security work, expect the downloadable model to differ from the hosted one.

Which One To Pick#

• Open weights matter to you, and you have a cluster or a hosting partner: K3.1 Pro becomes the strongest open option on Artificial Analysis's index once weights ship. Revisit on November 4.
• You want the lowest cost per finished coding task: run the head-to-head above before assuming Kimi wins; the launch-day evidence says it might not.
• You live in Claude Code: the Opus 5.5 release guide still describes the model Moonshot is benchmarking against. Nothing here forces a switch.

What People Are Actually Saying#

Reaction on r/LocalLLaMA is skeptical rather than hostile. These are search-result snippets and thread titles, not full thread reads, so treat the paraphrase accordingly.

• A thread titled "Is it just me or Kimi K3.1 Pro don't look much better than DeepSeek 4.1 Flash?" says the poster watched reviews of its output and was not impressed relative to a far cheaper model.
• A second thread, "Artificial Analysis Index is non-representative of real...", argues the index is easy to game and says K3.1 Pro costs about 10x more per task than DeepSeek on the Vals Index. That is a commenter's claim, not a figure we verified.
• A third, "Kimi K3.1 Pro AA revised down?", asks whether its Artificial Analysis score changed after launch. We did not confirm a revision.
• The counter-case, from Artificial Analysis rather than Reddit: its index puts K3.1 Pro only 3 points behind the best proprietary model once weights publish, so the capability gap is narrow even if the cost story is contested.

My read: the capability jump is real, the price cut is real, and the cost-per-task story is the part to test yourself.

Continue Reading#

• Kimi K3 in 10 Minutes - setup, pricing and benchmarks for the model K3.1 Pro builds on
• Kimi K3 open weights on Hugging Face - what the K3 license and download looked like
• DeepSeek V4.1 Flash 1017 guide - the cheap rival the Reddit threads compare against
• Gemini 4 Carbon developer guide - the other frontier model announced this week

FAQ#

How much does Kimi K3.1 Pro cost?#

$2 per million input tokens, $0.20 cached input and $10 per million output tokens, per Artificial Analysis. The Highspeed variant is priced at double, $4 and $20, per third-party reporting of Moonshot's pricing page.

When will Kimi K3.1 Pro open weights be released?#

Moonshot says the full weights will be released by November 4, 2026. The license has not been announced.

Is Kimi K3.1 Pro better than Claude Opus 5.5?#

On Moonshot's own Terminal-Bench 4.0 result it is a point behind (65.7% vs 66.4%), and Artificial Analysis ranks it fourth on its index behind Opus 5.5. It is cheaper per token, but cost per task is roughly equal to K3's.

What is the Kimi K3.1 Pro API model id?#

kimi-k3.1-pro, with kimi-k3.1-pro-highspeed for the faster variant, per Moonshot's launch post.

Sources#