// head_to_head
Claude Sonnet 5 vs SpaceXAI: Grok 4.6
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.
SpaceXAI: Grok 4.6 wins outright — it scores higher and costs less.
SpaceXAI: Grok 4.6 leads by 2.0 points overall while listing 33% 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. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.
anthropic
Claude Sonnet 5
- Blended / 1M
- $4.00
- Context
- 1M
- Released
- Jun 30, 2026
- Overall score
- 76.0
x-ai
SpaceXAI: Grok 4.6
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Aug 12, 2026
- Overall score
- 78.0
Specs and pricing
| Metric | Claude Sonnet 5 | SpaceXAI: Grok 4.6 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 76.0 | 78.0win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.2691 | $0.1181win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $3.00win |
| Input price / 1M | $2.00 | $2.00 |
| Output price / 1M | $10.00 | $6.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200win | $0.500 |
| Context window | 1Mwin | 500K |
| 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 — Claude Sonnet 5 on top, SpaceXAI: Grok 4.6 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 | Claude Sonnet 5 | SpaceXAI: Grok 4.6 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1150.40/mo | $852.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1480.00/mo | $1360.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1392.00/mo | $1240.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2660.00/mo | $2375.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5100.00/mo | $4850.00/mo |
Which should you pick?
You are running this at volume
SpaceXAI: Grok 4.6
Lowest measured cost per point of capability at $0.1181 per point — the gap compounds with every request.
Quality matters more than the bill
SpaceXAI: Grok 4.6
Highest overall LiveBench score of the two at 78.0.
The workload is coding or agentic work
Claude Sonnet 5
Leads on agentic coding — 59.4 against 57.0.
You need to fit large documents in one call
Claude Sonnet 5
Wider context window — 1M against 500K.
Claude Sonnet 5 vs SpaceXAI: Grok 4.6 FAQ
Which is better, Claude Sonnet 5 or SpaceXAI: Grok 4.6?
SpaceXAI: Grok 4.6 wins outright — it scores higher and costs less. SpaceXAI: Grok 4.6 leads by 2.0 points overall while listing 33% 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. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.
Is Claude Sonnet 5 cheaper than SpaceXAI: Grok 4.6?
SpaceXAI: Grok 4.6 is cheaper. On a 3:1 input:output blend, Claude Sonnet 5 lists at $4.00 per million tokens and SpaceXAI: Grok 4.6 at $3.00 — SpaceXAI: Grok 4.6 is 33% cheaper. Input and output are priced separately — Claude Sonnet 5 charges $2.00 in and $10.00 out, SpaceXAI: Grok 4.6 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Claude Sonnet 5 vs SpaceXAI: Grok 4.6: which scores higher on benchmarks?
Claude Sonnet 5 scores 76.0 and SpaceXAI: Grok 4.6 scores 78.0 overall on LiveBench, the mean of its seven categories. That is a 2.0-point lead for SpaceXAI: Grok 4.6. 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, Claude Sonnet 5 or SpaceXAI: Grok 4.6?
SpaceXAI: Grok 4.6. 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. Claude Sonnet 5 works out at $0.2691 per point and SpaceXAI: Grok 4.6 at $0.1181.
Does Claude Sonnet 5 or SpaceXAI: Grok 4.6 have a bigger context window?
Claude Sonnet 5 has the larger context window: 1M for Claude Sonnet 5 against 500K for SpaceXAI: Grok 4.6. 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 Claude Sonnet 5 and SpaceXAI: Grok 4.6 support prompt caching?
Both publish a cached-input rate: $0.200 per million for Claude Sonnet 5 and $0.500 for SpaceXAI: Grok 4.6, against full input rates of $2.00 and $2.00. 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.