// head_to_head

GPT-5.4 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Effectively the same quality — SpaceXAI: Grok 4.6 is the cheaper way to get it.

The two are within 0.1 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. SpaceXAI: Grok 4.6 lists 1.9× cheaper per blended million tokens. When quality ties, cost is the whole decision. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.

openai

GPT-5.4

Blended / 1M
$5.63
Context
1.1M
Released
Mar 5, 2026
Overall score
78.0
reasoningtool callingimage inputfile inputprompt caching

x-ai

SpaceXAI: Grok 4.6

Blended / 1M
$3.00
Context
500K
Released
Aug 12, 2026
Overall score
78.0
reasoningtool callingimage inputfile inputprompt caching

Specs and pricing

MetricGPT-5.4SpaceXAI: Grok 4.6
LiveBench overall

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

78.078.0
Cost per point

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

$0.2198$0.1181win
Blended price / 1M

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

$5.63$3.00win
Input price / 1M$2.50$2.00win
Output price / 1M$15.00$6.00win
Cached input / 1M

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

$0.250win$0.500
Context window1.1Mwin500K
Max output tokens128K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.4 on top, SpaceXAI: Grok 4.6 below, both out of 100.

Agentic coding
53.8
57.0
Codingtoo close to call
77.5
76.8
Reasoning
88.1
90.5
Mathematics
94.1
92.6
Data analysis
79.3
73.9
Language
82.6
83.7
Instruction following
70.2
71.9

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.

WorkloadGPT-5.4SpaceXAI: Grok 4.6
Support chatbot

1.2K in / 400 out × 200K requests

$1638.00/mo$852.00/mo
RAG assistant

8K in / 600 out × 100K requests

$2000.00/mo$1360.00/mo
Coding agent

40K in / 4K out × 20K requests

$1940.00/mo$1240.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$3512.50/mo$2375.00/mo
Bulk classification

500 in / 20 out × 5M requests

$6625.00/mo$4850.00/mo
Run these two through the cost calculator

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.

The workload is coding or agentic work

SpaceXAI: Grok 4.6

Leads on agentic coding — 57.0 against 53.8.

You need to fit large documents in one call

GPT-5.4

Wider context window — 1.1M against 500K.

GPT-5.4 vs SpaceXAI: Grok 4.6 FAQ

Which is better, GPT-5.4 or SpaceXAI: Grok 4.6?

Effectively the same quality — SpaceXAI: Grok 4.6 is the cheaper way to get it. The two are within 0.1 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. SpaceXAI: Grok 4.6 lists 1.9× cheaper per blended million tokens. When quality ties, cost is the whole decision. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.

Is GPT-5.4 cheaper than SpaceXAI: Grok 4.6?

SpaceXAI: Grok 4.6 is cheaper. On a 3:1 input:output blend, GPT-5.4 lists at $5.63 per million tokens and SpaceXAI: Grok 4.6 at $3.00 — SpaceXAI: Grok 4.6 is 1.9× cheaper. Input and output are priced separately — GPT-5.4 charges $2.50 in and $15.00 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.

GPT-5.4 vs SpaceXAI: Grok 4.6: which scores higher on benchmarks?

GPT-5.4 scores 78.0 and SpaceXAI: Grok 4.6 scores 78.0 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, GPT-5.4 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. GPT-5.4 works out at $0.2198 per point and SpaceXAI: Grok 4.6 at $0.1181.

Does GPT-5.4 or SpaceXAI: Grok 4.6 have a bigger context window?

GPT-5.4 has the larger context window: 1.1M for GPT-5.4 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 GPT-5.4 and SpaceXAI: Grok 4.6 support prompt caching?

Both publish a cached-input rate: $0.250 per million for GPT-5.4 and $0.500 for SpaceXAI: Grok 4.6, against full input rates of $2.50 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.
  • 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.