// head_to_head
Gemini 4 Hydrogen vs Kimi K3.1 Pro
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.
Kimi K3.1 Pro is the cheaper of the two; neither can be ranked on quality here.
Gemini 4 Hydrogen 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.
Gemini 4 Hydrogen
- Blended / 1M
- $3.00
- Context
- 1.0M
- Released
- Nov 12, 2026
- Overall score
- Not evaluated
moonshotai
Kimi K3.1 Pro
- Blended / 1M
- $2.28
- Context
- 1.0M
- Released
- Oct 23, 2026
- Overall score
- 68.7
Specs and pricing
| Metric | Gemini 4 Hydrogen | Kimi K3.1 Pro |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 68.7 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0107 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $3.00 | $2.28win |
| Input price / 1M | $1.50 | $0.237win |
| Output price / 1M | $7.50win | $8.41 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.075win | $0.200 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 944K | 944K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 4 Hydrogen on top, Kimi K3.1 Pro 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 | Gemini 4 Hydrogen | Kimi K3.1 Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $857.40/mo | $727.21/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,080/mo | $679.68/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,002/mo | $841.91/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,991/mo | $866.48/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3,787/mo | $1,416/mo |
Which should you pick?
You are cost-constrained
Kimi K3.1 Pro
Cheaper on blended list price at $2.28 per million tokens.
Gemini 4 Hydrogen vs Kimi K3.1 Pro FAQ
Which is better, Gemini 4 Hydrogen or Kimi K3.1 Pro?
Kimi K3.1 Pro is the cheaper of the two; neither can be ranked on quality here. Gemini 4 Hydrogen 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 4 Hydrogen cheaper than Kimi K3.1 Pro?
Kimi K3.1 Pro is cheaper. On a 3:1 input:output blend, Gemini 4 Hydrogen lists at $3.00 per million tokens and Kimi K3.1 Pro at $2.28 — Kimi K3.1 Pro is 32% cheaper. Input and output are priced separately — Gemini 4 Hydrogen charges $1.50 in and $7.50 out, Kimi K3.1 Pro charges $0.237 and $8.41 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Gemini 4 Hydrogen or Kimi K3.1 Pro have a bigger context window?
They are effectively the same — 1.0M for Gemini 4 Hydrogen and 1.0M for Kimi K3.1 Pro.
Do Gemini 4 Hydrogen and Kimi K3.1 Pro support prompt caching?
Both publish a cached-input rate: $0.075 per million for Gemini 4 Hydrogen and $0.200 for Kimi K3.1 Pro, against full input rates of $1.50 and $0.237. 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 2026-11-06, 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.