Kimi K3.1 Pro Pricing: How It Stacks Up Against Opus 5.5 and GPT-6 Astra
Kimi K3.1 Pro's API pricing runs $2 input/$10 output per million tokens, far below Opus 5.5 and GPT-6 Astra. Here's the real cost picture.
How much does Kimi K3.1 Pro cost?
Kimi K3.1 Pro, from the Chinese AI lab Moonshot AI, costs $2 per million input tokens and $10 per million output tokens through Moonshot’s API. That’s a cut of roughly a third from the previous K3 model. A faster variant, K3.1 Pro High Speed, runs two to three times quicker but costs double: $4 in and $20 out per million tokens. By comparison, Claude Opus 5.5 is priced around $4 input and $20 output, and GPT-6 Astra is reportedly around $10 input and $50 output. On paper, Kimi looks like roughly half the price of Opus and a fifth the price of Astra.
TL;DR
- Kimi K3.1 Pro’s list pricing ($2/$10 per million tokens) undercuts Claude Opus 5.5 (around $4/$20) and GPT-6 Astra (around $10/$50) by a wide margin.
- Moonshot’s own benchmark charts show K3.1 Pro beating Opus 5.5 and GPT-6 Astra on tests like Humanity’s Last Exam, SWE Marathon, and Office QA Pro, but these are largely self-reported numbers.
- Independent testing tells a more mixed story: Artificial Analysis ranks it fourth overall, and one independent coding benchmark (KingBench) scored it 53.7%, behind Opus, Astra, and even Haiku 5.5.
- The real cost story is in token usage, not just sticker price: one independent run found K3.1 Pro burns through huge volumes of cached context on every step, pushing a six-task benchmark run to around $32, more expensive than cheaper rivals that scored similarly or better.
- Cheaper doesn’t always mean better value: in that same test, Haiku 5.5 scored higher for about a quarter of the price, and DeepSeek V4.1 Flash came close to Kimi’s score for under $2.
- Moonshot plans to release K3.1 Pro’s weights as open source by a set date, which could let third-party hosts drive the effective price down further once it’s available outside Moonshot’s own API.
- The best economics for K3.1 Pro right now may come from a flat-rate Kimi subscription rather than pay-per-token API use, since heavy context rereading is what inflates the bill.
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What exactly do you get for $2/$10 per million tokens?
Kimi K3.1 Pro is a large mixture-of-experts model, reportedly around 2.8 trillion parameters total, with only about 104 billion active per token processed. That architecture is why Moonshot can price it so aggressively: you’re not paying to run the full model on every request, just the subset of “expert” parameters relevant to the task. It also ships with a 1 million token context window and supports image input alongside text.
The pricing applies across Moonshot’s ecosystem: the Kimi website and apps, Kimi Work (aimed at office and spreadsheet tasks), Kimi Code (a coding agent comparable to Claude Code or OpenAI’s Codex), and the raw API for developers. Cached input tokens are billed differently and more cheaply than fresh input tokens, which matters a lot for how the real-world bill shakes out, more on that below.
Is Kimi K3.1 Pro actually cheaper in practice, not just on paper?
This is where the headline pricing and the real cost diverge. One independent test, run through an open-source coding agent tool, tracked actual token consumption across six benchmark coding tasks. The model used around 112 million tokens across 793 requests, and at official rates, the entire run cost about $32 (around $42 if run on the High Speed variant).
The breakdown is the interesting part: K3.1 Pro only wrote under 700,000 output tokens, costing about $7. The other roughly $22 came from 110 million cached input tokens the model kept rereading at every step of its own agentic workflow. In other words, the model is “chatty” with its own context, repeatedly feeding itself the same information, and even cheap cached-token pricing adds up fast at that volume.
Compared against that same run, Haiku 5.5 scored higher for about $7.40 total. DeepSeek V4.1 Flash scored close to Kimi’s result for under $2. GPT-6.1 Soul and Gemini 4 Argon landed in similar score territory for single-digit dollar amounts. Opus 5.5 was the most expensive at $46 or more, but also scored clearly higher. In that specific test, K3.1 Pro ended up costing more than four times what Haiku cost, and about 16 times what DeepSeek Flash cost, without a performance edge to justify it.
How does Kimi K3.1 Pro’s performance compare to Opus 5.5 and GPT-6 Astra?
Moonshot’s own launch materials show K3.1 Pro ahead of both rivals on several tests: 70% versus 67.7% (Opus) and 57.2% (Astra) on Humanity’s Last Exam, 51.7 versus 46.3 and 42.5 on SWE Marathon, and 64.4 versus 62.2 on Office QA Pro, a finance and office-work benchmark Moonshot appears to have specifically targeted. On terminal bench, Moonshot reports 65.7%, close to Opus 5.5’s 66.4% and ahead of Astra’s 58.2%.
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Independent numbers are less flattering. Artificial Analysis, which runs its own cross-model intelligence index, scored K3.1 Pro at 55, putting it fourth overall behind Opus 5.5 (58) and tied rankings for Claude Sonnet 5.5 and Meta’s Muse Wonderful (56 each). On Arena’s web development leaderboard, it ranked eighth, a jump from 17th for the prior K3 model, but still behind Opus 5.5, Astra, and several others.
A separate independent coding benchmark (KingBench) scored K3.1 Pro at 53.7%, placing it sixth out of eleven tested models, behind Opus 5.5 (57.8%), Astra (56.5%), GPT-6.1 Soul (55.0%), Gemini 4 Argon (54.3%), and Haiku 5.5 (54.2%). That directly contradicts Moonshot’s claim of beating Astra by seven points.
Is Kimi K3.1 Pro worth it for API users?
For pay-per-token API use, the case is weak once you look past the sticker price. In the independent coding benchmark, no point on the price-to-performance chart favored K3.1 Pro: Haiku 5.5 delivered a slightly better score for roughly a quarter of the cost, DeepSeek V4.1 Flash came close to Kimi’s score for a small fraction of the price, and Opus 5.5, while priced at least 45% higher, delivered a performance gap large enough to justify the premium for demanding work.
The model does have genuine strengths worth noting. In task-level testing, it was notably honest about incomplete work (clearly flagging unimplemented features rather than faking them), handled a complex hardware protocol (VIA keyboard configuration) better than most models tested, and blocked every malicious container image thrown at it in a security test. Its hallucination rate, by Moonshot’s own internal test, dropped from 23.4% in K3 to 8.3% in K3.1 Pro on the harder tool-assisted version of that test, a real improvement if it holds up broadly.
Where the economics look better is outside the pay-per-token model: a flat-rate Kimi subscription, where token volume doesn’t directly hit your bill, sidesteps the context-rereading cost problem entirely. Running it through tools built specifically for Kimi’s agent protocol also seems to produce more consistent results than treating it as a drop-in API replacement for Opus or Astra.
What happens to the price once open weights are released?
Moonshot has said it will release K3.1 Pro’s full weights as open source, following the same pattern as the original K3 model, which is already available under Moonshot’s own license (broadly MIT-style with added conditions for very large companies). Once third parties can host the weights, competing providers may undercut Moonshot’s own API pricing, and organizations with sufficient hardware could run it internally without per-token costs at all. That said, this is a roughly 3-trillion-parameter-class model: self-hosting means a rack of GPUs, not a personal workstation. Moonshot has also indicated it stripped some training data (including cyber-related content) from the open release, meaning the downloadable version may not behave identically to the hosted API version.
Frequently Asked Questions
How much does Kimi K3.1 Pro cost per million tokens?
$2 for input tokens and $10 for output tokens through Moonshot’s API. The High Speed variant costs $4 input and $20 output.
Is Kimi K3.1 Pro cheaper than Claude Opus 5.5?
On list pricing, yes, roughly half the price. But independent testing found that heavy context rereading during agentic tasks can push real-world costs higher than the headline numbers suggest, and Opus still outperforms it on several independent benchmarks.
Does Kimi K3.1 Pro actually beat GPT-6 Astra?
Moonshot’s own benchmarks say yes on several tests, including Humanity’s Last Exam and finance-focused evaluations. Independent testing, including Artificial Analysis and a separate coding benchmark, found Astra ahead of K3.1 Pro overall.
Will Kimi K3.1 Pro get cheaper once it’s open source?
Likely, since third-party providers will be able to host the weights and compete on price once Moonshot releases them, as it did with the earlier K3 model.
Who should actually use Kimi K3.1 Pro right now?
It looks most cost-effective for users on a flat-rate Kimi subscription rather than pay-per-token API billing, and for workflows built around Kimi’s own agent tooling, where its context-heavy usage pattern is less likely to produce surprise costs.
