Kimi K3.1 Pro vs the Frontier: What the Benchmarks Actually Say

Open-weight models are no longer the "almost as good" understudy. For three years the pitch went the same way: the open model is cheaper, a bit worse, and fine if you squint. Kimi K3.1 Pro from Moonshot AI ends that routine. On independent testing it sits fourth in the world on general intelligence, third on real-world agentic work, and first on one of the hardest terminal-coding benchmarks in the field. Once the weights are published, every serious product team will have a real build-vs-buy decision to make, not a theoretical one.

What Kimi K3.1 Pro actually is

Kimi K3.1 Pro is Moonshot AI's latest flagship and the successor to Kimi K3. It is built for agentic and coding work, meaning models that plan, call tools, run commands and finish multi-step tasks, not just chat. According to Artificial Analysis, the model has:

• 2.8 trillion total parameters, 104 billion active (a mixture-of-experts design)
• A 1M-token context window
• Text and image input
• API pricing of $2 / $10 / $0.20 per million input, output and cached tokens
• Open weights announced but not yet published at launch

The last point matters most. An API-only model is one more vendor to rent from. An open-weight model at this level is something you can own, fine-tune and run wherever your compliance team wants it.

Independent rankings first

We trust independent numbers before vendor ones, every time. Artificial Analysis runs the same evaluations across every model it tests, so its numbers are the cleanest comparison available. Its Intelligence Index for launch week (23 October 2026):

• Claude Opus 5.5 (max, with fallback): 58
• Claude Sonnet 5.5 (max): 56
• Muse Wonder Full (max): 56
• Kimi K3.1 Pro: 55, fourth overall
• Kimi K3 (max): 44

Two things stand out. First, an 11-point jump over Kimi K3 in a single release is huge on an index this crowded. Second, Artificial Analysis notes that once the weights ship, K3.1 Pro will lead the open-weights category, and the gap between the best proprietary and best open model on this index shrinks from 12 points to 3. Three points is close enough that it can disappear behind prompt design, tooling and evaluation noise.

The broader scorecard moved too. Humanity's Last Exam rose from 47% to 52%, SciCode from 59% to 61%, and AA-Omniscience from 20 to 30. The hallucination rate fell from 53% to 44%. That is a real improvement, but 44% is still high. Don't put this model in front of customers without grounding and checks.

Where it wins: agents, terminals, automation

This is where K3.1 Pro stops being "a good open model" and becomes a top-tier model, full stop.

• GDPval-AA (real-world knowledge work, Elo): 1,846, third overall, behind Claude Opus 5.5 (1,866) and Muse Wonder Full (1,857). That is a 312-point jump over K3.
• Terminal-Bench 4.0 (Artificial Analysis run): 67%, the highest score of any model in the Artificial Analysis write-up, ahead of Claude Sonnet 5.5 at 64%. Kimi K3 scored 13%. That is not a typo.
• AutomationBench-AA: 73%, up from 58% on K3.

In plain terms, if your workload is "give the model a shell, a repo and a goal, then walk away," K3.1 Pro is competing for first place, not fourth.

Head-to-head

Model	Provider	AA Intelligence Index	Notes
Claude Opus 5.5 (max, w/ fallback)	Anthropic	58	#1 on AA index; GDPval-AA Elo 1,866 (top). Vendor sheet: Terminal-Bench 4.0 66.4%
Claude Sonnet 5.5 (max)	Anthropic	56	Terminal-Bench 4.0 (AA): 64%
Muse Wonder Full (max)	—	56	GDPval-AA Elo 1,857
Kimi K3.1 Pro	Moonshot AI	55	#4 overall; Terminal-Bench 4.0 (AA) 67%; GDPval-AA 1,846; open weights pending
GPT-6 Astra	OpenAI	—	Vendor sheet only: Terminal-Bench 4.0 58.2%
Gemini	Google	—	Not in the figures cited here
Kimi K3 (max)	Moonshot AI	44	Predecessor; Terminal-Bench 4.0 (AA) 13%

Where we haven't seen a comparable independent number, we left a dash. We would rather leave a gap than invent a figure.

Vendor scores vs independent scores

Moonshot's own launch sheet is vendor-run. Read it as marketing that also contains useful data. Its headline claim on Terminal-Bench 4.0:

• Claude Opus 5.5: 66.4%
• Kimi K3.1 Pro: 65.7%
• GPT-6 Astra: 58.2%

Oddly, Moonshot's own number (65.7%) is slightly lower than what Artificial Analysis measured independently (67%). Vendor and independent results rarely line up perfectly, because harnesses, effort settings and timeouts differ. When they land this close, the claim is credible.

Moonshot also claims strong results on SWE Marathon, OfficeQA Pro v2 and Humanity's Last Exam with tools. Its own sheet admits a weak spot: Terminal-Bench Science 0.1, where it trails GPT-6 Astra and Opus 5.5. We give the sheet more credit for showing a loss. Still, until those other claims are reproduced independently, treat them as leads to test, not facts to build a roadmap on.

Our rule: independent benchmarks tell you which models to shortlist. Your own evals on your own tasks tell you which one to ship.

Composite leaderboards such as LLMBenchmark.ai's November 2026 board also rank K3.1 Pro highly on coding among frontier models. That supports the picture, but Artificial Analysis remains our main independent source.

Cost, and when to pick it for a growth stack

Artificial Analysis puts K3.1 Pro at about $1.98 per Intelligence Index task, using roughly 55K output tokens per task. Per-token prices are low ($2 in, $10 out, $0.20 cached), but the model thinks at length. Don't judge it on the per-token rate alone. Judge it on cost per finished task.

Here's how we'd use it in a growth team's stack:

1. Coding agents and internal tooling. Terminal and automation work is where it is strongest. Use it for migrations, test generation, data pipelines and the CI chores nobody wants.
2. Product experiments at volume. Running hundreds of variant builds, landing-page tests or onboarding flows? A model this close to the frontier, at this price, changes the economics.
3. Regulated or data-sensitive workloads, once the weights land. For payments and fintech teams, which we have worked with since 2011, owning the model and where it runs can matter more than a 3-point index gap.
4. Not your customer-facing truth engine, yet. With a 44% hallucination rate, it needs retrieval, guardrails and human review anywhere accuracy is a brand risk.

What we would not do is chase the #1 slot on a leaderboard. Rankings change every month. A strong setup lets you swap models, routes each task to the model that does it best, and measures results against your own business metrics. Kimi K3.1 Pro has earned a place in that rotation.

The bottom line

Kimi K3.1 Pro is not the single best model in the world. Claude Opus 5.5 still leads the Artificial Analysis index. But it is fourth overall, third on agentic work, first on Terminal-Bench 4.0 in independent testing, and about to become open-weight. That combination makes the old "open is a compromise" argument hard to defend.

Want to know which model mix fits your product, your costs and your compliance constraints? Talk to the Eptune team. We'll help you test what the benchmarks can't.