Moonshot released Kimi K3.1 Pro on Friday, October 23, and priced it at $2 per million input tokens and $10 per million output tokens. Two days later, the launch carries two storylines. One is a benchmark table that places the model in the same conversation as Anthropic's Claude Opus 5.5 and OpenAI's GPT-6 Astra. The other is a set of unverified screenshots suggesting creators were told to hide paid promotion. Neither story is settled as of October 25.
Kimi K3.1 Pro pricing and where to access it
Moonshot made the model available on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, according to its technical blog post. The company describes Kimi K3.1 Pro as a 3-trillion-parameter model and a post-trained version of July's K3. Moonshot has also pledged open weights by November 4, 2026, but it has not said whether the downloadable version will match the API model.
For developers, price is the main argument. The per-token rate is low compared with US frontier models, which is why cost-focused teams are paying attention. A lower token rate does not guarantee a lower bill, though. In a single-test comparison, AtomicAgent found the cheaper rate cut task cost only when reasoning effort was turned down. Measure cost per finished task on your own workload, not cost per million tokens.
Benchmark scores and the ranking dispute
Artificial Analysis scored Kimi K3.1 Pro 55 on its Intelligence Index. Moonshot's own comparison shows that as an 11-point gain over Kimi K3, as Tech Insider reports. Where the model lands against Claude Opus 5.5 is less clear. aiweekly says Kimi ranks fourth and sits ahead of Opus 5.5, while Yotta Labs lists Opus 5.5 at 58 and puts Kimi three points behind. Both cannot be right on the same index, so check the live leaderboard before quoting a rank.
Moonshot's hallucination figures are the most striking. The company reports a claim-level hallucination rate of 8.3%, down from 23.4% for K3, and adds that these numbers "do not represent overall error rates in daily use." Artificial Analysis's AA-Omniscience hallucination rate moved from 53% to 44%. Those are different measures, so they should not be averaged or treated as the same test.
Cyber capability is the third number to watch. Artificial Analysis gives Kimi K3.1 Pro 64% on its Cyber Index. A third-party CellCog blog post, which is not independently verified, says only OpenAI's GPT-6 Sol scores higher, and OpenAI offers GPT-6 Sol only through trusted access. Moonshot says it removed cyber-related data from mid-training and the reinforcement-learning mix before the open release, trading some vulnerability-research capability for a safer open model.
Kimi K3.1 Pro against Claude Opus 5.5 and GPT-6 Astra
The head-to-head numbers depend on who ran the test. Moonshot's Terminal-Bench 4.0 figures and the Artificial Analysis comparison, as WinBuzzer reported it, point in opposite directions.
| Source | Kimi K3.1 Pro | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|---|
| Moonshot, Terminal-Bench 4.0 | 65.7% | 66.4% | 58.2% |
| Artificial Analysis, as reported by WinBuzzer | 67% | 60% | Not reported |
On Moonshot's table, Kimi trails Opus 5.5 by 0.7 points and leads GPT-6 Astra by 7.5 points. On the Artificial Analysis comparison, Kimi leads Opus by 7 points. Moonshot's own tables also mix sources, which makes the company's comparison harder to read than a single table suggests. If your team tests GPT-6 Astra alongside Kimi, note that USA 24 has reported that OpenAI's GPT-6 models now arrive on Google Cloud.
The gap is small enough that the winner depends on the test, not the brand. For most teams, cost per completed task and fit with existing tools will matter more than one leaderboard point. Moonshot gains a credible story at a moment when open-weight releases are arriving quickly. US labs face pressure on price only if these performance numbers hold up under independent testing.
The influencer allegations and what remains unverified
The allegations began with an X user named Elshayib, who posted screenshots that allegedly show a Kimi promotional brief. According to VentureBeat's reporting, one line reads: "Please DO NOT put on the paid partnership label." The document has not been independently authenticated, and no confirmed Moonshot response had surfaced as of October 25.
A later post from the same user, dated October 23, said he was asked to delete a comment after refusing the campaign. On October 24 he questioned whether the demonstrations creators shared were genuine, according to WinBuzzer. None of these claims has been independently verified.
VentureBeat reporter Carl Franzen addressed the benchmark side in the same coverage: "The allegations do not, by themselves, invalidate independent benchmark results." That distinction matters. The promotion story does not, on its own, show the model is weaker. If the allegations were proven, VentureBeat notes, undisclosed paid endorsements would conflict with various US laws, which would matter to any brand or creator involved in the campaign.
Is Kimi K3.1 Pro safe to use for business?
"Safe" means at least three things here: whether outputs are reliable, whether benchmark claims hold up, and where your data goes. The hallucination and cyber figures address the first two, and both are contested. The third question is one procurement teams cannot skip. VentureBeat advises weighing data-handling terms and deployable weights, not just benchmarks.
An open-weight model from a Chinese lab also raises questions that security teams will ask. USA 24 has reported that the Department of Homeland Security ordered federal agencies to pull DeepSeek, including third-party tools. None of the sources reviewed for this story reports a comparable action on Kimi K3.1 Pro. Teams should check current policy rather than assume that the absence of action means approval.
If you want to test the model this week, start here:
- Run your own tasks using real code, documents or agent workflows, and measure cost and success per completed task.
- Read the data-handling and retention terms for the Kimi API before any company or customer data goes in.
- Keep sensitive workloads off the hosted service until your security review signs off.
- If you plan to self-host, wait for the November 4 weights and confirm they match the API version.
What to watch before November 4
The open-weight deadline is the first real test of Moonshot's claims. Independent researchers will be able to run cyber, hallucination and coding tests on downloadable weights, which will settle more than any launch table. Watch three things: whether the released weights match the API model, whether Artificial Analysis and other leaderboards settle the Opus 5.5 ranking, and whether Moonshot responds to the screenshots.
The field is also getting crowded. Meta launched Muse Spark Open on October 15, an open-weight model that runs on Macs, according to TechCrunch. On October 5, Reflection AI, a Brooklyn startup, unveiled its open-weight Beam model as a rival to Chinese open models, TechCrunch reported. Each new release resets the price and performance comparison for teams choosing between hosted and self-hosted models.
If the weights ship on time and independent tests hold, Kimi K3.1 Pro becomes a cheap, testable option in a crowded field. If the weights slip or the screenshots prove authentic, the disclosure questions will matter more than the price.
