// head_to_head
Kimi K2 Thinking vs SpaceXAI: Grok Build 0.1
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.
Kimi K2 Thinking is the cheaper of the two; neither can be ranked on quality here.
Kimi K2 Thinking 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 K2 Thinking
- Blended / 1M
- $1.07
- Context
- 262K
- Released
- Nov 6, 2025
- Overall score
- Not evaluated
x-ai
SpaceXAI: Grok Build 0.1
- Blended / 1M
- $1.25
- Context
- 256K
- Released
- May 20, 2026
- Overall score
- 67.8
Specs and pricing
| Metric | Kimi K2 Thinking | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 67.8 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0144 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.07win | $1.25 |
| Input price / 1M | $0.600win | $1.00 |
| Output price / 1M | $2.50 | $2.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.150win | $0.200 |
| Context window | 262K | 256K |
| Max output tokens | 98K | 230Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K2 Thinking on top, SpaceXAI: Grok Build 0.1 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 K2 Thinking | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $311.60/mo | $342.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $450.00/mo | $600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $428.00/mo | $512.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $765.00/mo | $1,110/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,525/mo | $2,300/mo |
Which should you pick?
You are cost-constrained
Kimi K2 Thinking
Cheaper on blended list price at $1.07 per million tokens.
Kimi K2 Thinking vs SpaceXAI: Grok Build 0.1 FAQ
Which is better, Kimi K2 Thinking or SpaceXAI: Grok Build 0.1?
Kimi K2 Thinking is the cheaper of the two; neither can be ranked on quality here. Kimi K2 Thinking 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 K2 Thinking cheaper than SpaceXAI: Grok Build 0.1?
Kimi K2 Thinking is cheaper. On a 3:1 input:output blend, Kimi K2 Thinking lists at $1.07 per million tokens and SpaceXAI: Grok Build 0.1 at $1.25 — Kimi K2 Thinking is 16% cheaper. Input and output are priced separately — Kimi K2 Thinking charges $0.600 in and $2.50 out, SpaceXAI: Grok Build 0.1 charges $1.00 and $2.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Kimi K2 Thinking or SpaceXAI: Grok Build 0.1 have a bigger context window?
They are effectively the same — 262K for Kimi K2 Thinking and 256K for SpaceXAI: Grok Build 0.1.
Do Kimi K2 Thinking and SpaceXAI: Grok Build 0.1 support prompt caching?
Both publish a cached-input rate: $0.150 per million for Kimi K2 Thinking and $0.200 for SpaceXAI: Grok Build 0.1, against full input rates of $0.600 and $1.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
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 2026-06-25, 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.