// head_to_head
GPT-5.6 Luna 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.
GPT-5.6 Luna wins outright — it scores higher and costs less.
GPT-5.6 Luna leads by 5.8 points overall while listing 2.8× cheaper per blended million tokens. 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. The two cost measures disagree here, which is worth knowing: GPT-5.6 Luna has the lower sticker price, but SpaceXAI: Grok Build 0.1 earns each point of capability for less — $0.0144 against $0.0911 — because per-token rates do not predict how many tokens a model actually spends on a task.
openai
GPT-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 73.6
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 | GPT-5.6 Luna | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 73.6win | 67.8 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0911 | $0.0144win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.450win | $1.25 |
| Input price / 1M | $0.200win | $1.00 |
| Output price / 1M | $1.20win | $2.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.200 |
| Context window | 1.1Mwin | 256K |
| Max output tokens | 128K | — |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.6 Luna 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 | GPT-5.6 Luna | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $131.04/mo | $342.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $160.00/mo | $600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $155.20/mo | $512.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $281.00/mo | $1110.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $530.00/mo | $2300.00/mo |
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
GPT-5.6 Luna
Highest overall LiveBench score of the two at 73.6.
The workload is coding or agentic work
GPT-5.6 Luna
Leads on agentic coding — 48.4 against 45.8.
You need to fit large documents in one call
GPT-5.6 Luna
Wider context window — 1.1M against 256K.
GPT-5.6 Luna vs SpaceXAI: Grok Build 0.1 FAQ
Which is better, GPT-5.6 Luna or SpaceXAI: Grok Build 0.1?
GPT-5.6 Luna wins outright — it scores higher and costs less. GPT-5.6 Luna leads by 5.8 points overall while listing 2.8× cheaper per blended million tokens. 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. The two cost measures disagree here, which is worth knowing: GPT-5.6 Luna has the lower sticker price, but SpaceXAI: Grok Build 0.1 earns each point of capability for less — $0.0144 against $0.0911 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is GPT-5.6 Luna cheaper than SpaceXAI: Grok Build 0.1?
GPT-5.6 Luna is cheaper. On a 3:1 input:output blend, GPT-5.6 Luna lists at $0.450 per million tokens and SpaceXAI: Grok Build 0.1 at $1.25 — GPT-5.6 Luna is 2.8× cheaper. Input and output are priced separately — GPT-5.6 Luna charges $0.200 in and $1.20 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.
GPT-5.6 Luna vs SpaceXAI: Grok Build 0.1: which scores higher on benchmarks?
GPT-5.6 Luna scores 73.6 and SpaceXAI: Grok Build 0.1 scores 67.8 overall on LiveBench, the mean of its seven categories. That is a 5.8-point lead for GPT-5.6 Luna. 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, GPT-5.6 Luna 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. GPT-5.6 Luna works out at $0.0911 per point and SpaceXAI: Grok Build 0.1 at $0.0144.
Does GPT-5.6 Luna or SpaceXAI: Grok Build 0.1 have a bigger context window?
GPT-5.6 Luna has the larger context window: 1.1M for GPT-5.6 Luna 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 GPT-5.6 Luna and SpaceXAI: Grok Build 0.1 support prompt caching?
Both publish a cached-input rate: $0.020 per million for GPT-5.6 Luna and $0.200 for SpaceXAI: Grok Build 0.1, against full input rates of $0.200 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.