// head_to_head
Claude Haiku 5.5 vs SpaceXAI: Grok 4.5
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.
Claude Haiku 5.5 wins outright — it scores higher and costs less.
Claude Haiku 5.5 leads by 4.4 points overall while listing 15× 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. Claude Haiku 5.5 also leads on measured cost per point of capability, at $0.1429 per point.
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Claude Haiku 5.5
- Blended / 1M
- $0.200
- Context
- 1M
- Released
- Oct 7, 2026
- Overall score
- 74.5
x-ai
SpaceXAI: Grok 4.5
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Jul 8, 2026
- Overall score
- 70.1
Specs and pricing
| Metric | Claude Haiku 5.5 | SpaceXAI: Grok 4.5 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.5win | 70.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1429win | $0.5008 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200win | $3.00 |
| Input price / 1M | $0.100win | $2.00 |
| Output price / 1M | $0.500win | $6.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010win | $0.300 |
| Context window | 1Mwin | 500K |
| Max output tokens | 128K | 450Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Haiku 5.5 on top, SpaceXAI: Grok 4.5 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 Haiku 5.5 | SpaceXAI: Grok 4.5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $837.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $1,280/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $1,128/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $2,365/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $4,750/mo |
Which should you pick?
You are running this at volume
Claude Haiku 5.5
Lowest measured cost per point of capability at $0.1429 per point — the gap compounds with every request.
Quality matters more than the bill
Claude Haiku 5.5
Highest overall LiveBench score of the two at 74.5.
The workload is coding or agentic work
SpaceXAI: Grok 4.5
Leads on coding — 94.3 against 84.5.
You need to fit large documents in one call
Claude Haiku 5.5
Wider context window — 1M against 500K.
Claude Haiku 5.5 vs SpaceXAI: Grok 4.5 FAQ
Which is better, Claude Haiku 5.5 or SpaceXAI: Grok 4.5?
Claude Haiku 5.5 wins outright — it scores higher and costs less. Claude Haiku 5.5 leads by 4.4 points overall while listing 15× 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. Claude Haiku 5.5 also leads on measured cost per point of capability, at $0.1429 per point.
Is Claude Haiku 5.5 cheaper than SpaceXAI: Grok 4.5?
Claude Haiku 5.5 is cheaper. On a 3:1 input:output blend, Claude Haiku 5.5 lists at $0.200 per million tokens and SpaceXAI: Grok 4.5 at $3.00 — Claude Haiku 5.5 is 15× cheaper. Input and output are priced separately — Claude Haiku 5.5 charges $0.100 in and $0.500 out, SpaceXAI: Grok 4.5 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Claude Haiku 5.5 vs SpaceXAI: Grok 4.5: which scores higher on benchmarks?
Claude Haiku 5.5 scores 74.5 and SpaceXAI: Grok 4.5 scores 70.1 overall on LiveBench, the mean of its seven categories. That is a 4.4-point lead for Claude Haiku 5.5. 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 Haiku 5.5 or SpaceXAI: Grok 4.5?
Claude Haiku 5.5. 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 Haiku 5.5 works out at $0.1429 per point and SpaceXAI: Grok 4.5 at $0.5008.
Does Claude Haiku 5.5 or SpaceXAI: Grok 4.5 have a bigger context window?
Claude Haiku 5.5 has the larger context window: 1M for Claude Haiku 5.5 against 500K for SpaceXAI: Grok 4.5. 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 Haiku 5.5 and SpaceXAI: Grok 4.5 support prompt caching?
Both publish a cached-input rate: $0.010 per million for Claude Haiku 5.5 and $0.300 for SpaceXAI: Grok 4.5, against full input rates of $0.100 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 2026-09-02, 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.