// head_to_head
Claude Haiku 5.5 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.
Effectively the same quality — Claude Haiku 5.5 is the cheaper way to get it.
The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Claude Haiku 5.5 lists 15× cheaper per blended million tokens. When quality ties, cost is the whole decision. Claude Haiku 5.5 also leads on measured cost per point of capability, at $0.1429 per point.
anthropic
Claude Haiku 5.5
- Blended / 1M
- $0.200
- Context
- 1M
- Released
- Oct 7, 2026
- Overall score
- 74.5
x-ai
SpaceXAI: Grok 4.6
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Aug 12, 2026
- Overall score
- 74.6
Specs and pricing
| Metric | Claude Haiku 5.5 | SpaceXAI: Grok 4.6 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.5 | 74.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1429win | $0.8427 |
| 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 | $0.500 |
| 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 4.6 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 Haiku 5.5 | SpaceXAI: Grok 4.6 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $852.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $1,360/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $1,240/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $2,375/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $4,850/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.
The workload is coding or agentic work
SpaceXAI: Grok 4.6
Leads on coding — 89.1 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 4.6 FAQ
Which is better, Claude Haiku 5.5 or SpaceXAI: Grok 4.6?
Effectively the same quality — Claude Haiku 5.5 is the cheaper way to get it. The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Claude Haiku 5.5 lists 15× cheaper per blended million tokens. When quality ties, cost is the whole decision. 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 4.6?
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 4.6 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 4.6 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 4.6: which scores higher on benchmarks?
Claude Haiku 5.5 scores 74.5 and SpaceXAI: Grok 4.6 scores 74.6 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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 4.6?
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 4.6 at $0.8427.
Does Claude Haiku 5.5 or SpaceXAI: Grok 4.6 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 4.6. 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 Haiku 5.5 and SpaceXAI: Grok 4.6 support prompt caching?
Both publish a cached-input rate: $0.010 per million for Claude Haiku 5.5 and $0.500 for SpaceXAI: Grok 4.6, 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.