# Gemini 3.1 Pro vs Kimi K3

On provider list prices, Gemini 3.1 Pro costs $2 per million input tokens against $3 for Kimi K3: 1.5x apart. Output is $12 against $15 (1.3x).

## Specifications

| | Gemini 3.1 Pro | Kimi K3 |
| --- | --- | --- |
| Lab | Google | Moonshot AI |
| Access | API only | Open weights |
| Context window | 1M tokens | 1M tokens |
| List price, input | $2 / M tokens | $3 / M tokens |
| List price, output | $12 / M tokens | $15 / M tokens |
| Cached input | n/a | $0.30 / M tokens |
| License | Proprietary API | Not listed |
| Fine-tunable | No | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $6,600 a month on Gemini 3.1 Pro and $8,850 on Kimi K3 at list: a gap of $2,250, or 1.3x.

## Choose Gemini 3.1 Pro for

- Judgment-heavy workflows
- Long-context analysis
- Escalation tier above Flash

## Choose Kimi K3 for

- Open weights you can fine-tune and own
- Published cached-input pricing ($0.30 per M tokens)

## Common questions

### Which is cheaper, Gemini 3.1 Pro or Kimi K3?

Gemini 3.1 Pro, on this workload shape. At list prices it is $2/$12 per million tokens in and out against $3/$15 for Kimi K3. Billed on Allocate: $2.14/$12.84 against $3.21/$16.05.

### Which has the bigger context window?

They match: both read 1,000,000 tokens (1M) per request.

### Can I fine-tune Gemini 3.1 Pro or Kimi K3?

Kimi K3 publishes open weights (Not listed) and can be fine-tuned on your own data. Gemini 3.1 Pro is a closed model served over API; its weights are not available.

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[HTML page](https://allocate.network/compare/google-gemini-3-1-pro-vs-moonshotai-kimi-k3) · [Gemini 3.1 Pro](https://allocate.network/models/google-gemini-3-1-pro.md) · [Kimi K3](https://allocate.network/models/moonshotai-kimi-k3.md) · [Machine-readable catalog](https://allocate.network/catalog.json)