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Kimi K3 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Kimi K3 scores higher, SpaceXAI: Grok Build 0.1 costs less — it depends on your workload.

Kimi K3 is ahead by 11.4 points overall, and SpaceXAI: Grok Build 0.1 lists 4.8× cheaper per blended million tokens. Whether 11.4 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok Build 0.1 also leads on measured cost per point of capability, at $0.0144 per point.

moonshotai

Kimi K3

Blended / 1M
$6.00
Context
1.0M
Released
Jul 16, 2026
Overall score
79.2
reasoningtool callingimage inputvideo inputprompt caching

x-ai

SpaceXAI: Grok Build 0.1

Blended / 1M
$1.25
Context
256K
Released
May 20, 2026
Overall score
67.8
reasoningtool callingimage inputfile inputprompt caching

Specs and pricing

MetricKimi K3SpaceXAI: Grok Build 0.1
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

79.2win67.8
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.1909$0.0144win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$6.00$1.25win
Input price / 1M$3.00$1.00win
Output price / 1M$15.00$2.00win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.300$0.200win
Context window1.0Mwin256K
Max output tokens

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K3 on top, SpaceXAI: Grok Build 0.1 below, both out of 100.

Agentic coding
62.2
45.8
Coding
81.4
65.4
Reasoning
90.7
76.4
Mathematics
84.4
78.4
Data analysis
78.7
70.8
Language
85.5
72.5
Instruction following
71.4
65.2

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.

WorkloadKimi K3SpaceXAI: Grok Build 0.1
Support chatbot

1.2K in / 400 out × 200K requests

$1725.60/mo$342.40/mo
RAG assistant

8K in / 600 out × 100K requests

$2220.00/mo$600.00/mo
Coding agent

40K in / 4K out × 20K requests

$2088.00/mo$512.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$3990.00/mo$1110.00/mo
Bulk classification

500 in / 20 out × 5M requests

$7650.00/mo$2300.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

SpaceXAI: Grok Build 0.1

Lowest measured cost per point of capability at $0.0144 per point — the gap compounds with every request.

Quality matters more than the bill

Kimi K3

Highest overall LiveBench score of the two at 79.2.

The workload is coding or agentic work

Kimi K3

Leads on agentic coding — 62.2 against 45.8.

You need to fit large documents in one call

Kimi K3

Wider context window — 1.0M against 256K.

Kimi K3 vs SpaceXAI: Grok Build 0.1 FAQ

Which is better, Kimi K3 or SpaceXAI: Grok Build 0.1?

Kimi K3 scores higher, SpaceXAI: Grok Build 0.1 costs less — it depends on your workload. Kimi K3 is ahead by 11.4 points overall, and SpaceXAI: Grok Build 0.1 lists 4.8× cheaper per blended million tokens. Whether 11.4 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok Build 0.1 also leads on measured cost per point of capability, at $0.0144 per point.

Is Kimi K3 cheaper than SpaceXAI: Grok Build 0.1?

SpaceXAI: Grok Build 0.1 is cheaper. On a 3:1 input:output blend, Kimi K3 lists at $6.00 per million tokens and SpaceXAI: Grok Build 0.1 at $1.25 — SpaceXAI: Grok Build 0.1 is 4.8× cheaper. Input and output are priced separately — Kimi K3 charges $3.00 in and $15.00 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.

Kimi K3 vs SpaceXAI: Grok Build 0.1: which scores higher on benchmarks?

Kimi K3 scores 79.2 and SpaceXAI: Grok Build 0.1 scores 67.8 overall on LiveBench, the mean of its seven categories. That is a 11.4-point lead for Kimi K3. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Kimi K3 or SpaceXAI: Grok Build 0.1?

SpaceXAI: Grok Build 0.1. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Kimi K3 works out at $0.1909 per point and SpaceXAI: Grok Build 0.1 at $0.0144.

Does Kimi K3 or SpaceXAI: Grok Build 0.1 have a bigger context window?

Kimi K3 has the larger context window: 1.0M for Kimi K3 against 256K for SpaceXAI: Grok Build 0.1. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Kimi K3 and SpaceXAI: Grok Build 0.1 support prompt caching?

Both publish a cached-input rate: $0.300 per million for Kimi K3 and $0.200 for SpaceXAI: Grok Build 0.1, against full input rates of $3.00 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.
  • ScoresLiveBench 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.