// head_to_head

Gemini 3.6 Flash vs SpaceXAI: Grok 4.20 Multi-Agent

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

Gemini 3.6 Flash and SpaceXAI: Grok 4.20 Multi-Agent are priced within ~10% of each other.

SpaceXAI: Grok 4.20 Multi-Agent does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

google

Gemini 3.6 Flash

Blended / 1M
$1.50
Context
1.0M
Released
Jul 21, 2026
Overall score
73.6
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

x-ai

SpaceXAI: Grok 4.20 Multi-Agent

Blended / 1M
$1.56
Context
2M
Released
Mar 31, 2026
Overall score
Not evaluated
reasoningimage inputfile inputprompt caching

Specs and pricing

MetricGemini 3.6 FlashSpaceXAI: Grok 4.20 Multi-Agent
LiveBench overall

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

73.6
Cost per point

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

$0.1327
Blended price / 1M

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

$1.50$1.56
Input price / 1M$0.750win$1.25
Output price / 1M$3.75$2.50win
Cached input / 1M

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

$0.075win$0.200
Context window1.0M2Mwin
Max output tokens66K1.8Mwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.6 Flash on top, SpaceXAI: Grok 4.20 Multi-Agent below, both out of 100.

Agentic coding
43.4
Coding
77.9
Reasoning
85.1
Mathematics
86.4
Data analysis
63.0
Language
83.9
Instruction following
75.4

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.

WorkloadGemini 3.6 FlashSpaceXAI: Grok 4.20 Multi-Agent
Support chatbot

1.2K in / 400 out × 200K requests

$431.40/mo$424.40/mo
RAG assistant

8K in / 600 out × 100K requests

$555.00/mo$730.00/mo
Coding agent

40K in / 4K out × 20K requests

$522.00/mo$612.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$997.50/mo$1,385/mo
Bulk classification

500 in / 20 out × 5M requests

$1,912/mo$2,850/mo
Run these two through the cost calculator

Which should you pick?

You need to fit large documents in one call

SpaceXAI: Grok 4.20 Multi-Agent

Wider context window — 2M against 1.0M.

Gemini 3.6 Flash vs SpaceXAI: Grok 4.20 Multi-Agent FAQ

Which is better, Gemini 3.6 Flash or SpaceXAI: Grok 4.20 Multi-Agent?

Gemini 3.6 Flash and SpaceXAI: Grok 4.20 Multi-Agent are priced within ~10% of each other. SpaceXAI: Grok 4.20 Multi-Agent does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is Gemini 3.6 Flash cheaper than SpaceXAI: Grok 4.20 Multi-Agent?

They cost about the same. Both land near $1.50 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.

Does Gemini 3.6 Flash or SpaceXAI: Grok 4.20 Multi-Agent have a bigger context window?

SpaceXAI: Grok 4.20 Multi-Agent has the larger context window: 1.0M for Gemini 3.6 Flash against 2M for SpaceXAI: Grok 4.20 Multi-Agent. 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 Gemini 3.6 Flash and SpaceXAI: Grok 4.20 Multi-Agent support prompt caching?

Both publish a cached-input rate: $0.075 per million for Gemini 3.6 Flash and $0.200 for SpaceXAI: Grok 4.20 Multi-Agent, against full input rates of $0.750 and $1.25. 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.