// head_to_head
Gemma 4 31B 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 scores higher, Gemma 4 31B costs less — it depends on your workload.
Kimi K3.1 Pro is ahead by 32.6 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 32.6 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Gemma 4 31B
- Blended / 1M
- $0.250
- Context
- 262K
- Released
- Apr 2, 2026
- Overall score
- 38.7
moonshotai
Kimi K3.1 Pro
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 23, 2026
- Overall score
- 71.3
Specs and pricing
| Metric | Gemma 4 31B | Kimi K3.1 Pro |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 38.7 | 71.3win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0017win | $0.0104 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.250win | $4.00 |
| Input price / 1M | $0.200win | $2.00 |
| Output price / 1M | $0.400win | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.200 |
| Context window | 262K | 1.0Mwin |
| Max output tokens | 16K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemma 4 31B 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 | Gemma 4 31B | Kimi K3.1 Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $80.00/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $184.00/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $192.00/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $230.00/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $540.00/mo | $5,100/mo |
Which should you pick?
You are running this at volume
Gemma 4 31B
Lowest measured cost per point of capability at $0.0017 per point — the gap compounds with every request.
Quality matters more than the bill
Kimi K3.1 Pro
Highest overall LiveBench score of the two at 71.3.
The workload is coding or agentic work
Kimi K3.1 Pro
Leads on long-horizon agentic — 39.1 against 10.7.
You need to fit large documents in one call
Kimi K3.1 Pro
Wider context window — 1.0M against 262K.
Gemma 4 31B vs Kimi K3.1 Pro FAQ
Which is better, Gemma 4 31B or Kimi K3.1 Pro?
Kimi K3.1 Pro scores higher, Gemma 4 31B costs less — it depends on your workload. Kimi K3.1 Pro is ahead by 32.6 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 32.6 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Is Gemma 4 31B cheaper than Kimi K3.1 Pro?
Gemma 4 31B is cheaper. On a 3:1 input:output blend, Gemma 4 31B lists at $0.250 per million tokens and Kimi K3.1 Pro at $4.00 — Gemma 4 31B is 16× cheaper. Input and output are priced separately — Gemma 4 31B charges $0.200 in and $0.400 out, Kimi K3.1 Pro charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemma 4 31B vs Kimi K3.1 Pro: which scores higher on benchmarks?
Gemma 4 31B scores 38.7 and Kimi K3.1 Pro scores 71.3 overall on LiveBench, the weighted mean of its seven categories. That is a 32.6-point lead for Kimi K3.1 Pro. 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, Gemma 4 31B or Kimi K3.1 Pro?
Gemma 4 31B. 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. Gemma 4 31B works out at $0.0017 per point and Kimi K3.1 Pro at $0.0104.
Does Gemma 4 31B or Kimi K3.1 Pro have a bigger context window?
Kimi K3.1 Pro has the larger context window: 262K for Gemma 4 31B against 1.0M for Kimi K3.1 Pro. 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 Gemma 4 31B and Kimi K3.1 Pro support prompt caching?
Kimi K3.1 Pro publishes a cached-input rate of $0.200 per million tokens against a full input rate of $2.00. The catalogue lists no separate cached rate for Gemma 4 31B, which means the provider does not price it separately here — not that caching is unavailable.
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 2026-10-25, 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.