Kimi K3.1 Pro costs $2/$10 per million tokens, half of Claude Opus 5.5, and scores 65.7% on Terminal-Bench 4.0 vs Opus 5.5's 66.4%. Here's the full breakdown.
Vamsi Tallapudi
Manager, Architect Technology at Cognizant
Moonshot AI shipped Kimi K3.1 Pro on October 23, 2026, and the pitch is blunt: a 3-trillion-parameter-class open-weight model priced at $2 per million input tokens and $10 per million output tokens, roughly half of what Claude Opus 5.5 charges, while landing close to Opus 5.5 on coding benchmarks. Full weights are due by November 4, 2026. If that holds, this becomes the first open-weight model to score this close to the closed frontier.
I've been skeptical of every "open model beats closed model" headline this year because the fine print usually hides a narrower benchmark or a quota wall. This one's fine print is more interesting than most: Moonshot deliberately gutted the model's hacking ability before shipping it, and the pricing story only works if you ignore how fast people are burning through Kimi Code's usage caps. Let's get into what's actually true.
Kimi K3.1 Pro is Moonshot AI's upgrade to July 2026's Kimi K3, keeping the same 2.8 trillion total / 104 billion active parameter mixture-of-experts architecture and 1-million-token context window, but with a heavier post-training pass. Moonshot calls it "3T-class" because that's close enough to 3 trillion to round up for marketing, even though the real total is 2.8T.
It ships in two flavors:
Both are live now on Kimi.ai, the Kimi mobile apps, Kimi Work, Kimi Code, and the API. The 1M context window is gated to Pro-tier subscribers, and Highspeed needs a Pro membership too — Plus gets you the regular model.
The model also adds three selectable reasoning effort levels (low, medium, max), so you can trade latency for depth on a per-request basis instead of being stuck with whatever the model decides to do.
Here's the number that's driving the coverage. Opus 5.5 runs $4 input / $20 output per million tokens. Kimi K3.1 Pro's standard tier is exactly half that. Even the faster Highspeed variant only catches up to Opus 5.5's price, not exceeds it.
| Model | Input ($/M) | Output ($/M) | Notes |
|---|---|---|---|
| Kimi K3.1 Pro | $2.00 | $10.00 | Down from K3's $3/$15 |
| Kimi K3.1 Pro Highspeed | $4.00 | $20.00 | 2-3x faster, matches Opus 5.5 price |
| Claude Opus 5.5 | $4.00 | $20.00 | Cache reads at $0.20/M |
| GPT-6.1 Sol | $2.00 | $10.00 | Same tier as standard K3.1 Pro |
That table is the whole business case for Kimi K3.1 Pro in one glance. It's not pretending to beat Opus 5.5 outright. It's selling "close enough, half the price," which is a pitch that's worked for Chinese open models all year.
Moonshot's own line is that K3.1 Pro lands "approximately level with Opus 5.5 and GPT-6 Astra." The independent numbers mostly back that up, with Opus 5.5 still ahead on the benchmark that matters most for a coding audience.
| Benchmark | Kimi K3.1 Pro | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|---|
| Terminal-Bench 4.0 (coding agents) | 65.7% | 66.4% | 58.2% |
| Artificial Analysis Intelligence Index | 55 | 58 | 53 |
| BenchCAD (3D/CAD generation) | 92.7% | ~96% | ~95% |
| OfficeQA Pro v2 (finance) | 64.4 | lower | lower |
On Terminal-Bench, K3.1 Pro is 0.7 points behind Opus 5.5 and 7.5 points ahead of GPT-6 Astra. That's a real result, not a cherry-picked one. Artificial Analysis puts it 4th overall on its Intelligence Index at a score of 55, ahead of Claude Fable 5.1, GPT-6 Astra, and Gemini 4 Argon, all tied at 53, but still three points under Opus 5.5.
Hallucinations dropped a lot too. Moonshot's internal claim-level error rate fell from 23.4% on K3 to 8.3% on K3.1 Pro. That's the kind of improvement that actually changes whether you'd trust the model's output in a report without double-checking every sentence.
One thing worth flagging if you care about verbosity-driven cost: during evaluation, K3.1 Pro generated around 150 million tokens, more chatty than the median model (87M) but nowhere near Opus 5.5's 260M. If you're paying per output token, that gap compounds fast.
This is the part most "price vs benchmark" writeups skip, and it's the most interesting decision in the release. Moonshot explicitly removed cyberattack-related training data before shipping K3.1 Pro, and they say so directly: "K3.1 Pro's offensive cyber capability is, by design, below what our frontier models offer."
The numbers show it. On internal exploit-solving benchmarks, an unfiltered version of the model hit 90.5%. The version Moonshot actually shipped scored 81.0%. For comparison, K3 scored 33.3% on the same test, and DeepSeek's V4.1-Flash scored 85.7%. So K3.1 Pro's hacking ability went up a lot from K3, just not as far up as it could have gone.
Why bother? Because this model's weights are going fully open on November 4. Once that happens, anyone can fine-tune away whatever safety training Moonshot applied. Stripping the underlying knowledge from the base model is a much harder thing to undo than stripping a refusal behavior, and it's a reasonable response to the fact that Moonshot's earlier K3 release had already drawn scrutiny over concerning security behavior. Whether it's enough is a different question, and nobody will really know until the weights are out and people start poking at them.
If you're pricing out API usage, yes, it's worth a look — half the cost of Opus 5.5 for benchmark results that are within a point of it on Terminal-Bench is a legitimately good deal, and the hallucination drop matters if you're using it for anything beyond toy snippets.
But the subscription side is messier than the per-token pricing suggests. People testing Kimi Code on the Hacker News thread about this release weren't complaining about benchmarks, they were complaining about the 5-hour usage quota. One user reported burning through their session limit in 47 minutes. If your actual workflow is "use Kimi Code all day," the headline price doesn't mean much when you hit a wall before lunch. Claude, DeepSeek, and other competitors don't squeeze subscribers that hard, and that's the detail that decides whether cheap-per-token actually translates into cheap-in-practice.
My take: for API calls at your own pace, K3.1 Pro is a solid budget alternative to Opus 5.5 right now, especially once the open weights land and you can self-host instead of trusting a quota. For anyone leaning on the subscription app for sustained coding sessions, wait and see if Moonshot loosens the quota before switching your daily driver.
If you've been tracking how the earlier Kimi K3 stacked up against Claude for frontend work, our Kimi K3 vs Claude breakdown still holds up as a baseline, and you can see where Opus 5.5 itself landed on pricing in our Opus 5.5 pricing review.
Kimi K3.1 Pro costs $2 per million input tokens and $10 per million output tokens through the Kimi API. That's half of Claude Opus 5.5's $4/$20 rate and matches GPT-6.1 Sol's pricing.
Highspeed is the same model weights running 2-3x faster, priced at $4 per million input tokens and $20 per million output tokens, roughly double the standard tier and equal to what Claude Opus 5.5 costs.
Moonshot AI says full model weights will be released by November 4, 2026. Until then, the model is only usable through Kimi's apps and paid API.
On Terminal-Bench 4.0, Kimi K3.1 Pro scores 65.7% versus Opus 5.5's 66.4%, a 0.7-point gap. Both beat GPT-6 Astra's 58.2% on the same benchmark.
Because the weights are going open-source. Moonshot stripped cyberattack training data so the released model solves 81% of internal exploit tasks instead of 90.5%, making it harder for anyone to fine-tune strong offensive cyber skills back in after the open release.
Early Kimi Code subscribers reported hitting their 5-hour usage quota in under an hour of active use, which undercuts the per-token price advantage for anyone doing sustained coding sessions rather than occasional API calls.
Yes. It handles text and images, supports a 1-million-token context window on Pro-tier access, and Moonshot highlights strengths in full-project coding (Unreal, Unity, Godot), parametric 3D modeling in Blender and CAD tools, and financial document analysis.
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