Kimi K3.1 Pro is Moonshot AI's most capable open-weight model, released on 23 October 2026. It scores 65.7% on Terminal-Bench 4.0, just 0.7 points behind Claude Opus 5.5, at roughly half the price, and its full weights are promised by 4 November. For developers, it is the first open model that is genuinely close to the frontier rather than a cheaper compromise.
Key takeaways
- Kimi K3.1 Pro scores 65.7% on Terminal-Bench 4.0 in Moonshot's own numbers, 0.7 points behind Claude Opus 5.5 (66.4%) and ahead of GPT-6 Astra (58.2%). Artificial Analysis independently measured 67%.
- It ranks 4th of 215 models on the Artificial Analysis Intelligence Index with a score of 55. The open-versus-closed gap has shrunk from 12 points to 3.
- Pricing is $2 input and $10 output per million tokens, about half of Claude Opus 5.5. A Highspeed variant runs 2 to 3 times faster at the same price.
- It is the same 2.8-trillion-parameter mixture-of-experts architecture as Kimi K3, with 104 billion parameters active per token and a 1-million-token context. The gain comes from post-training, not a bigger model.
- The full weights are promised by 4 November and are not yet downloadable. Moonshot deliberately trimmed the model's cyber capability before release, cutting its internal exploit-bench score from 90.5% to 81.0%.
How close is Kimi K3.1 Pro to the frontier, really?
Close enough that the gap is now measured in fractions of a point on the benchmark developers care about most. Terminal-Bench 4.0 tests coding agents on real terminal tasks: installing dependencies, running tests, fixing broken builds. Moonshot reports 65.7% for Kimi K3.1 Pro, against 66.4% for Claude Opus 5.5 and 58.2% for GPT-6 Astra. Artificial Analysis, which runs the benchmark independently, measured 67%, which would be the highest Terminal-Bench score any model has posted. That independent confirmation matters, because vendor-reported launch numbers have a poor record this month: ARC Prize independently reran GPT-6 Astra at 62.7% on its own benchmark, against the 99.9% OpenAI had published from its own harness.
The picture holds up beyond one benchmark. On the Artificial Analysis Intelligence Index, which blends several evaluations into a single score, Kimi K3.1 Pro scores 55 and sits 4th out of 215 models, behind only Claude Opus 5.5 and Sonnet. On GDPval-AA, a board of real-world work tasks, it takes third place at 1,846 Elo, behind Claude Opus 5.5 at 1,866 and Muse Wonder Full at 1,857. Moonshot also claims first place on SWE Marathon, its long-horizon coding benchmark, at 51.7%, and a state-of-the-art 64.4 on OfficeQA Pro v2 for financial document work. Those last two are Moonshot's own claims and have not been independently rerun yet, so treat them as claims, not rankings.
The honest reading: near-frontier at coding and agentic work, with a wider gap on science-heavy tasks, where Moonshot's own charts show it trailing. If your workload is long coding sessions or terminal agents, this is the interesting case. For graduate-level science reasoning, the frontier models still lead by more.
What actually changed since Kimi K3?
Nothing about the architecture. Kimi K3.1 Pro is explicitly a post-trained K3: the same 2.8-trillion-parameter mixture-of-experts design, the same 104 billion active parameters per token, the same 1-million-token context, the same Kimi Delta Attention and Stable LatentMoE internals. What changed is the training after the base model: joint reinforcement learning across hundreds of downstream domains, on a rebuilt RL stack, with infrastructure optimisation at every layer from sandbox tooling to rollout scheduling.
The reported effects are large. Moonshot claims its hallucination rate on Kimi Hallu Bench Hard fell from 23.4% to 8.3%, and its long-horizon coding score on Kimi Code Bench 3.0 rose from 55.95% for K3 to 61.2%. Both are internal benchmarks, so trust the direction more than the exact numbers. The Terminal-Bench jump makes it concrete: K3 scored around 13% on the same benchmark family; K3.1 Pro scores 65.7%. That is not a tweak. It learned to finish multi-step terminal work instead of stalling halfway.
There is a lesson in this for anyone choosing models. The frontier is no longer decided only by who trains the biggest base model. Post-training, reinforcement learning on agentic tasks, and better tooling now move scores by tens of points. A lab that invests in the second half of the pipeline can close most of the gap without matching the first half's compute budget.
How much does it cost to run?
At $2 per million input tokens and $10 per million output tokens, Kimi K3.1 Pro costs about half of Claude Opus 5.5 while scoring within a point of it on coding-agent benchmarks. Artificial Analysis calls this a new benchmark for intelligence per dollar. The Highspeed variant, kimi-k3.1-pro-highspeed, promises 2 to 3 times the speed at the same price, which matters in agent loops where latency compounds across dozens of tool calls.
Price per token is only half the story, though. What decides your bill is tokens per finished task, and K3.1 Pro is an always-thinking model: it cannot turn reasoning off. Simple calls still burn reasoning tokens, so a short classification or extraction job may cost more here than on a smaller model even at the lower rate card. The right way to use it is for the hard, long-horizon work where its agent scores pay off, and to keep cheap models for the routine calls. That is the same split we recommended in our guide to cutting LLM API bills: route by task difficulty, not by brand loyalty.
Why did Moonshot make it weaker on purpose?
This is the most interesting part of the release, and the part most coverage skipped. As training progressed, cyber capability emerged: the unfiltered model solved 90.5% of a hard internal exploit benchmark. Before releasing the weights, Moonshot removed cyber-related data from mid-training and reinforcement learning, dropping that score to 81.0% at pass@3. The company told Reuters: "We aim not to release open weights that would extend the absolute frontier of cyber capability."
That is a new kind of release decision. Open-weight releases used to be a question of capability and licence; Moonshot treats them as a safety decision too: what the weights enable in the worst hands, not just what they do in the best benchmark. The trade is real, 9.5 points of exploit capability given up, and it sets a template other open-weight labs are likely to copy. The model you will download on 4 November is deliberately not the strongest version Moonshot built. For ordinary coding that costs nothing; for security research it is a genuine limitation, better known now than discovered later.
How do you try it today?
The weights are not downloadable yet, but the model is live now through Moonshot's API, the Kimi app, Kimi Code, and kimi.ai. The API is OpenAI-compatible, so existing code needs only the model name changed:
from openai import OpenAI
client = OpenAI(
api_key="YOUR_MOONSHOT_API_KEY",
base_url="https://api.moonshot.ai/v1",
)
response = client.chat.completions.create(
model="kimi-k3.1-pro",
messages=[
{"role": "user", "content": "Refactor this function to use a generator and explain the trade-off."},
],
temperature=0.2,
)
print(response.choices[0].message.content)
For agent loops, switch the model string to kimi-k3.1-pro-highspeed and keep everything else the same. Two practical notes: the 1-million-token context is real, but filling it is expensive, so pass only the files and history the agent actually needs; and if you handle customer data, check the Zero Data Retention option on the API, which Moonshot offers for API customers.
Before you migrate a production agent, run your own evaluation. Pick ten tasks your current model handles, run them through K3.1 Pro at temperature 0.2, and score pass or fail yourself. Vendor benchmarks measure the vendor's tasks. Your tasks are the only ones that decide your bill.
What is still unknown?
Three things to watch before you plan around this model. First, the weights are promised by 4 November, not shipped, and no licence has been published yet. Until the weights and licence actually land, "open-weight" is a promise, not a fact, and self-hosting plans should stay provisional. Second, a 2.8-trillion-parameter mixture-of-experts model with 104 billion active parameters per token will need serious multi-GPU hardware or aggressive quantisation to run locally, and nobody outside Moonshot knows the memory footprint yet. Third, the independent numbers we have so far cover coding and general intelligence; the financial-document and long-horizon claims are Moonshot's alone and await independent reruns.
Conclusion
Kimi K3.1 Pro is the first open-weight model genuinely close to the frontier on the work developers pay for: long coding sessions and terminal agents. It scores 65.7% on Terminal-Bench 4.0 against Claude Opus 5.5's 66.4%, at about half the price, and its independently measured 67% may be the best score on that benchmark so far. The caveats are real: the weights are not out yet, the licence is unpublished, and the model was deliberately weakened on cyber capability. But the direction is unmistakable. Open models stopped being a compromise this month. On 4 November, anyone with enough hardware will be able to run a model three points off the frontier. That changes the build-versus-buy calculation for every team running coding agents.
Originally published on LogicStack. Run the code examples right in your browser with our free interactive Python and SQL playgrounds — no setup, no sign-up.
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