// head_to_head
Kimi K3.1 Pro vs Crouton Alpha
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Crouton Alpha is the cheaper of the two; neither can be ranked on quality here.
Crouton Alpha does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
moonshotai
Kimi K3.1 Pro
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 23, 2026
- Overall score
- 71.3
stealth
Crouton Alpha
- Blended / 1M
- Free
- Context
- 1.0M
- Released
- Nov 3, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Kimi K3.1 Pro | Crouton Alpha |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 71.3 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0104 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | Freewin |
| Input price / 1M | $2.00 | Freewin |
| Output price / 1M | $10.00 | Freewin |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | — |
| Context window | 1.0M | 1.0M |
| Max output tokens | 944K | 944K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K3.1 Pro on top, Crouton Alpha below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Kimi K3.1 Pro | Crouton Alpha |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,150/mo | $0/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,480/mo | $0/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,392/mo | $0/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,660/mo | $0/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,100/mo | $0/mo |
Which should you pick?
You are cost-constrained
Crouton Alpha
Cheaper on blended list price at Free per million tokens.
Kimi K3.1 Pro vs Crouton Alpha FAQ
Which is better, Kimi K3.1 Pro or Crouton Alpha?
Crouton Alpha is the cheaper of the two; neither can be ranked on quality here. Crouton Alpha does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Kimi K3.1 Pro cheaper than Crouton Alpha?
Crouton Alpha is cheaper. On a 3:1 input:output blend, Kimi K3.1 Pro lists at $4.00 per million tokens and Crouton Alpha at Free. Input and output are priced separately — Kimi K3.1 Pro charges $2.00 in and $10.00 out, Crouton Alpha charges Free and Free — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Kimi K3.1 Pro or Crouton Alpha have a bigger context window?
They are effectively the same — 1.0M for Kimi K3.1 Pro and 1.0M for Crouton Alpha.
Do Kimi K3.1 Pro and Crouton Alpha support prompt caching?
Kimi K3.1 Pro publishes a cached-input rate of $0.200 per million tokens against a full input rate of $2.00. The catalogue lists no separate cached rate for Crouton Alpha, which means the provider does not price it separately here — not that caching is unavailable.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-10-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.