TL;DR: Kimi K3.1 Pro launched October 23 as a heavily RL-post-trained Kimi K3 — same 2.8T total / 104B active architecture, same 1M context, open weights promised by November 4. No consumer rig ran K3 and none will run K3.1 Pro. What actually changed for a home lab: the API now costs $2/$10 per million tokens, a third less than K3.

	K3.1 Pro API	Rented H200 pod	Your own GPU
Best for	Almost everyone	Data that can’t leave your infra	Models that actually fit
Cost	$2 in / $10 out per M tokens	~$70/hr for a 16× H200 footprint	Used RTX 3090: $1,456–$1,558
The catch	Prompts leave your machine	Weights alone are ~1.56TB	K3.1 Pro will never load on it

Honest take: Don’t buy anything for this model, and don’t start the download on November 4. Use the cheaper API, keep a used RTX 3090 as your local workhorse, and wait for the distills — that’s where open 2.8T weights eventually pay off for people without a data center.

Moonshot AI shipped Kimi K3.1 Pro on October 23, 2026, and the announcement is doing big numbers — the Hacker News thread crossed 1,000 points within a day, mostly on two hooks: vendor benchmarks that put an open-weight-to-be model within half a point of Claude Opus 5.5 on Terminal-Bench 4.0, and a promise that the full weights go up by November 4.

We’ve been here before. When Kimi K3’s weights landed in July, the question every home-labber asked was “what do I need to run it,” and the answer was a number with no consumer interpretation: 1.56TB of weights, an 8× GB300 node minimum. K3.1 Pro is the same architecture — Moonshot says so directly; this is a post-training release, not a new model — so that answer hasn’t moved a byte. What has moved is the price of not running it yourself, and that’s the part worth your attention.

What K3.1 Pro actually is

Strip the version bump and the “Pro” suffix: K3.1 Pro is Kimi K3 with a much larger reinforcement-learning post-training run across what Moonshot describes as hundreds of domains (Crowdfund Insider has the launch framing). The architecture is unchanged from the K3 spec sheet we verified in July:

Spec	Value (inherited from K3)
Total parameters	2.8T
Activated per token	104B
Experts	896 total, 16 selected + 2 shared
Context window	1,048,576 tokens
Modality	Text, images, video in (MoonViT-V2 encoder)
Native precision	MXFP4 weights / MXFP8 activations (QAT)

Some launch coverage rounds the model up to “3 trillion parameters” (The Tribune runs that headline); MarkTechPost carries the precise 2.8T / 104B figures, which match the K3 config exactly. Trust the config.

Two things ship alongside the standard model. A Highspeed variant (kimi-k3.1-pro-highspeed) generates 2–3× faster at double the price — $4 in / $20 out versus the standard $2 in / $10 out per million tokens (Basic Tutorials confirms both tiers). And there’s a deliberate subtraction: Moonshot says it stripped offensive-cyber training data from this release so the open weights stay behind the frontier on hacking tasks (Notebookcheck) — the first time we’ve seen a lab pre-emptively degrade a capability because the weights are about to be public.

The benchmarks, with the asterisks attached

Every launch number is vendor-reported, and Moonshot ran the agentic coding evals inside the Claude Code harness at maximum reasoning effort (TestmuAI dug that out of the footnotes). With that disclaimer standing:

• Terminal-Bench 4.0: 65.7% — under Claude Opus 5.5 (66.4%), above GPT-6 Astra (58.2%) per Tech-Insider. Note the version: this is the harder Terminal-Bench 4.0, not the 2.1 suite where K3 scored 88.3 in July and where we quote most site numbers. The two scales don’t compare.
• Terminal-Bench-Science: 38.1% — the clear weak spot, versus 63.3% for Opus 5.5 and 68.1% for GPT-6 Astra.
• SWE-Marathon v1.1: 51.7 — early coverage mislabeled this as “SWE-bench”; OrcaRouter’s benchmark audit traced the 51.7 to SWE-Marathon, and several sources corroborate.
• Artificial Analysis Intelligence Index: 55 — an 11-point jump over K3, and the one number on the sheet from an independent harness.
• GDPval-AA: 4th of 358 models; AutomationBench-AA: 2nd of 32 (Benchmarklist).

The picture: a real post-training gain on agentic work, a genuine hole on scientific terminal tasks, and no independent replication of the headline rows yet. Same posture we took with Nanbeige4.3-33B two days ago: believe the direction, hold the magnitudes loosely until third parties run them.

The hardware math: nothing changed, and that’s the point

Because K3.1 Pro is architecturally identical to K3, every hard number from our July serving breakdown carries over:

Capacity. The K3 weights are 1.56TB in their native MXFP4 release — 4-bit is the full-precision format here, trained that way from the SFT stage. Expect the K3.1 Pro repo to land within rounding distance of the same size (we’ll verify the actual shard total when it posts; until then treat 1.56TB as an estimate inherited from the identical parent). All 2.8T parameters must sit in memory because the router selects different experts every token. vLLM’s stated minimum for K3 is a single node of 8× GB300-class GPUs — 2.3TB of pooled HBM3e. An 8× H200 node (1.13TB pooled) doesn’t even hold the weights; H200-class serving means 16 GPUs across two nodes before the first token of KV cache.

Bandwidth. The “but it’s MoE, only 104B is active” hope doesn’t rescue anything. At MXFP4, 104B active parameters mean roughly 52GB of weight reads per token. Run that through the decode ceiling we use for every GPU on this site (bandwidth ÷ bytes-per-token): an RTX 5090’s 1,792 GB/s tops out around 34 tok/s if the weights were resident — and the 5090 holds 2% of them. The capacity wall arrives long before the bandwidth wall matters. Punch your own card into the VRAM calculator if you want the comparison to sting locally.

Throughput when properly hosted. vLLM’s published day-0 K3 numbers remain the reference: 118 tok/s per request on 16× GB300, 370 tok/s with DSpark speculative decoding enabled.

The quant fantasy. Community quants below the native 4 bits trade real quality for shrink, and even a hypothetical 2-bit build lands north of 700GB. That’s still four Mac Studios clustered over Thunderbolt 5, not a PC. If you want trillion-class weights on hardware you can actually assemble, Kimi K2.7 Code’s 325GB 2-bit build remains the realistic ceiling: a 384GB-DDR5 CPU box or four used RTX 3090s plus 256GB of system RAM, grinding out 8–12 tok/s.

Don’t assume the license — K3’s history says wait for the file

Early aggregator posts are already calling the November 4 release “Apache 2.0 expected.” Be careful. The exact same prediction circulated before K3’s July drop — “Modified MIT, like Moonshot’s earlier releases” — and what shipped was neither: a custom “Kimi K3 License,” MIT-style in practice but with a $20M Model-as-a-Service revenue gate and a UI badge clause above 100M MAU (OpenRouter’s license review covers why it doesn’t qualify as OSI open source; our July analysis read the full text).

For a home lab the practical difference was nil — K3’s license freely permits local inference, fine-tunes, and GGUF redistribution below those thresholds, and K3.1 Pro will most plausibly ship the same paper. But “most plausibly” is not a license. If you’re building anything commercial on these weights, read the LICENSE file in the actual repo on November 4, not the headlines on October 26.

The llama.cpp reality check, October edition

Three months after K3’s weights landed, mainline llama.cpp still has no kimi_k3 architecture. Every community GGUF on Hugging Face still dies the same way:

$ llama-cli -m Kimi-K3-IQ2_XXS-00001-of-00017.gguf -p "hello"
llama_model_load: error loading model: error loading model architecture:
unknown model architecture: 'kimi_k3'

No flag or conversion trick fixes an unsupported architecture — we’ve covered why. The state of play as of October 26:

• PR #26185 (kimi-k3-text) is the upstream implementation effort; Unsloth’s K3 docs build their working GGUFs against that branch — you can run K3 today on Unsloth’s fork if you own ~768GB of memory, which you don’t.
• The KDA linear-attention kernels — the hard part of K3’s 69 KDA layers — actually reached mainline on September 30, when GLM-5.3-Flash support merged reusing Kimi-K3 KDA code. The plumbing is arriving; the architecture flag isn’t merged.

When K3.1 Pro’s weights post, expect GGUF conversions within hours and expect none of them to load on mainline. Same trap, new filename. Don’t spend the bandwidth.

What changed for your wallet: the API got 33% cheaper

Here’s the part of this launch that actually touches a home-lab budget. Kimi K3’s API ran $3 per million input tokens / $15 output. K3.1 Pro launches at $2 / $10 — a better model at two-thirds the price, available through the Kimi API and routed via OpenRouter under kimi-k3.1-pro (Cellcog and Basic Tutorials both carry the launch pricing; Moonshot hasn’t published a cached-input rate for K3.1 Pro yet, versus K3’s $0.30 cached floor).

Run the self-hosting comparison at the new price. A 16× H200 footprint costs about $70/hr at RunPod’s $4.39/hr per H200 (their pricing page, updated July 2026; B200s run $5.89). At $10 per million output tokens, that hour of pod rent buys 7 million output tokens from the API — you’d need to sustain multi-million-token-per-hour agentic throughput around the clock before the pod wins on price. The three legitimate pod cases are unchanged: data that legally can’t leave your infrastructure, fine-tuning (if the license permits it — see above), and saturated parallel serving. Our cloud GPU pricing guide has the full provider math.

For coding agents specifically — the workload K3.1 Pro is tuned for — the $2/$10 price undercuts every closed frontier model’s list price, which is exactly the squeeze Moonshot is going for. If you’re wiring it into Cursor, Cline, or a Claude Code-compatible harness as a BYOK backend, our sister site’s agent tooling coverage tracks that side; the self-hosted serving stack side (vLLM, SGLang) lives at aifoss.dev.

What to actually buy

Prices as of October 2026, all verified in the comparison above or our price index:

Your situation	The move	Price	Where
You want K3.1 Pro’s output tonight	Kimi API / OpenRouter	$2 in / $10 out per M tokens	kimi.ai
Data can’t leave your infrastructure	Rented 16× H200 pod, vLLM	~$70/hr	RunPod
You want the best model your own card holds	Used RTX 3090 24GB	$1,456–$1,558	Check price
Trillion-class weights you physically own	4× used RTX 3090 + 256GB RAM → K2.7 2-bit, not K3.1	~$5,800–$6,250 in cards alone	Check price
Not sure any of this is worth it yet	Rent a 3090 and test your workload	from $0.07/hr	Vast.ai

The middle rows deserve one honest sentence each. A used RTX 3090 doesn’t run K3.1 Pro and never will — it runs the 24GB-class leaders that cover most daily local work, which is the correct consolation prize. And the quad-3090 row is there for the reader who genuinely wants trillion-parameter weights at home: that budget buys the K2.7 setup at 8–12 tok/s, and no budget below data-center scale buys K3.1 Pro.

The real home-lab payoff: distills

The reason November 4 still matters to people with one GPU: open 2.8T weights are distillation feedstock. K3’s July release started that pipeline; a stronger post-trained checkpoint restarts it from a higher baseline. When the first K3.1-distilled 27B–70B models appear — and after the October leaderboard churn, they will — those are the artifacts that change what your card can do. Watch for them; skip the 1.6TB torrent.

FAQ

Can I run Kimi K3.1 Pro on my GPU? No. Same architecture as Kimi K3: expect ~1.56TB of native MXFP4 weights and an 8× GB300-class node (2.3TB pooled) as the vLLM minimum. No consumer card or multi-GPU home rig comes within an order of magnitude.

Is Kimi K3.1 Pro open source? Not yet, and the license is unconfirmed. Weights are promised by November 4, 2026. K3 shipped under a custom Moonshot license (free in practice below a $20M revenue gate) after coverage predicted Modified MIT — read the actual LICENSE file when the repo posts before assuming “Apache 2.0 expected” headlines are right.

What’s different from Kimi K3? Post-training only: a large RL run that lifts agentic benchmarks (Terminal-Bench 4.0 65.7%, vendor-reported; Artificial Analysis Index 55, +11 over K3), a 2–3× faster Highspeed API variant at $4/$20, deliberately weakened offensive-cyber ability, and a cheaper standard API at $2/$10 versus K3’s $3/$15.

Will Ollama or LM Studio support it? Both wait on llama.cpp, where kimi_k3 support is still an open PR (#26185) as of October 26 — though the KDA attention kernels reached mainline via the GLM-5.3-Flash merge on September 30. Even with support, sub-native quants stay above ~700GB. This is not an Ollama model and won’t become one.

Should a home lab builder buy hardware for the November 4 release? No. Use the API ($2/$10), rent H200s only if data sovereignty forces it (~$70/hr for 16×), and keep your local money on a used RTX 3090 tier card. Revisit when K3.1 distills land in the 27B–70B range.

Recommended Gear

• Used RTX 3090 24GB — $1,456–$1,558 on the used market (October 2026); still the best VRAM-per-dollar home for the models that actually fit in one card.

Sources

• Moonshot AI Releases Kimi K3.1 Pro: A 2.8T Total/104B Active Params (MoE) Model with 1M Context — MarkTechPost
• Kimi K3.1 Pro: 1M Context, 3T Open Weights — Tech-Insider
• Kimi K3.1 Pro Benchmarks: Every Score, Who Produced It, and Where the Table Is Still Blank — OrcaRouter
• Kimi K3.1 Pro: Benchmarks, API Changes, and Open Weights — TestmuAI
• Kimi K3.1 Pro: Moonshot’s New AI Model at a Competitive Price — Basic Tutorials
• Kimi K3.1 Pro: Free to try, hacking skills deliberately weakened — Notebookcheck
• Kimi K3.1 Pro: Price, Benchmarks and Open Weights Date — Cellcog
• China’s Moonshot AI Unveils Kimi K3.1 Pro — Crowdfund Insider
• Kimi K3 official repo: spec sheet and license — Moonshot AI / GitHub
• Is Kimi K3 Open Source? Weights, License, and How to Call It — OpenRouter
• Kimi K3 GGUF status and PR #26185 — Unsloth Docs
• llama.cpp Merges GLM-5.3-Flash with Kimi-K3 KDA kernel reuse — AI Weekly
• RunPod GPU Cloud Pricing — RunPod

Last updated October 26, 2026. Prices, API rates, and release dates change; verify current numbers before purchasing or building.

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