// head_to_head

Ox Alpha 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

Ox Alpha wins outright — it scores higher and costs less.

Ox Alpha leads by 1.5 points overall. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. Ox Alpha also leads on measured cost per point of capability, at $0.0000 per point.

stealth

Ox Alpha

Blended / 1M
Free
Context
1.0M
Released
Aug 20, 2026
Overall score
69.2
reasoningtool callingimage inputvideo input

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

MetricOx AlphaSpaceXAI: Grok Build 0.1
LiveBench overall

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

69.2win67.8
Cost per point

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

$0.0000win$0.0144
Blended price / 1M

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

Freewin$1.25
Input price / 1MFreewin$1.00
Output price / 1MFreewin$2.00
Cached input / 1M

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

$0.200
Context window1.0Mwin256K
Max output tokens131K

Benchmarks by category

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

Agentic coding
52.6
45.8
Coding
75.8
65.4
Reasoningtoo close to call
76.6
76.4
Mathematicstoo close to call
77.5
78.4
Data analysis
75.8
70.8
Language
66.1
72.5
Instruction following
60.3
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.

WorkloadOx AlphaSpaceXAI: Grok Build 0.1
Support chatbot

1.2K in / 400 out × 200K requests

$0/mo$342.40/mo
RAG assistant

8K in / 600 out × 100K requests

$0/mo$600.00/mo
Coding agent

40K in / 4K out × 20K requests

$0/mo$512.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$0/mo$1110.00/mo
Bulk classification

500 in / 20 out × 5M requests

$0/mo$2300.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Ox Alpha

Lowest measured cost per point of capability at $0.0000 per point — the gap compounds with every request.

Quality matters more than the bill

Ox Alpha

Highest overall LiveBench score of the two at 69.2.

The workload is coding or agentic work

Ox Alpha

Leads on agentic coding — 52.6 against 45.8.

You need to fit large documents in one call

Ox Alpha

Wider context window — 1.0M against 256K.

Ox Alpha vs SpaceXAI: Grok Build 0.1 FAQ

Which is better, Ox Alpha or SpaceXAI: Grok Build 0.1?

Ox Alpha wins outright — it scores higher and costs less. Ox Alpha leads by 1.5 points overall. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. Ox Alpha also leads on measured cost per point of capability, at $0.0000 per point.

Is Ox Alpha cheaper than SpaceXAI: Grok Build 0.1?

Ox Alpha is cheaper. On a 3:1 input:output blend, Ox Alpha lists at Free per million tokens and SpaceXAI: Grok Build 0.1 at $1.25. Input and output are priced separately — Ox Alpha charges Free in and Free 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.

Ox Alpha vs SpaceXAI: Grok Build 0.1: which scores higher on benchmarks?

Ox Alpha scores 69.2 and SpaceXAI: Grok Build 0.1 scores 67.8 overall on LiveBench, the mean of its seven categories. That is a 1.5-point lead for Ox Alpha. 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, Ox Alpha or SpaceXAI: Grok Build 0.1?

Ox Alpha. 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. Ox Alpha works out at $0.0000 per point and SpaceXAI: Grok Build 0.1 at $0.0144.

Does Ox Alpha or SpaceXAI: Grok Build 0.1 have a bigger context window?

Ox Alpha has the larger context window: 1.0M for Ox Alpha 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 Ox Alpha and SpaceXAI: Grok Build 0.1 support prompt caching?

SpaceXAI: Grok Build 0.1 publishes a cached-input rate of $0.200 per million tokens against a full input rate of $1.00. The catalogue lists no separate cached rate for Ox 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.
  • 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.