# Gemini 3.7 Flash vs NVIDIA Nemotron 3 Ultra 550B A55B NVFP4

On provider list prices, NVIDIA Nemotron 3 Ultra 550B A55B NVFP4 costs $0.60 per million input tokens against $0.75 for Gemini 3.7 Flash: 1.3x apart. Output is $3.60 against $3.75.

## Specifications

| | Gemini 3.7 Flash | NVIDIA Nemotron 3 Ultra 550B A55B NVFP4 |
| --- | --- | --- |
| Lab | Google | NVIDIA |
| Access | API only | API only |
| Context window | 1M tokens | 512K tokens |
| List price, input | $0.75 / M tokens | $0.60 / M tokens |
| List price, output | $3.75 / M tokens | $3.60 / M tokens |
| Cached input | n/a | $0.20 / M tokens |
| License | Proprietary API | Proprietary API |
| Fine-tunable | No | No |

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 $1,980 a month on NVIDIA Nemotron 3 Ultra 550B A55B NVFP4 and $2,213 on Gemini 3.7 Flash at list: a gap of $232.50.

Gemini 3.7 Flash reads 1M tokens per request against 512K for NVIDIA Nemotron 3 Ultra 550B A55B NVFP4, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose Gemini 3.7 Flash for

- The longer context window (1M vs 512K tokens)

## Choose NVIDIA Nemotron 3 Ultra 550B A55B NVFP4 for

- The lower list price ($0.60 in / $3.60 out per M tokens)
- Published cached-input pricing ($0.20 per M tokens)

## Common questions

### Which is cheaper, Gemini 3.7 Flash or NVIDIA Nemotron 3 Ultra 550B A55B NVFP4?

NVIDIA Nemotron 3 Ultra 550B A55B NVFP4, on this workload shape. At list prices it is $0.60/$3.60 per million tokens in and out against $0.75/$3.75 for Gemini 3.7 Flash. Billed on Allocate: $0.64/$3.85 against $0.80/$4.01.

### Which has the bigger context window?

Gemini 3.7 Flash: 1,000,000 tokens (1M) against 512,288 (512K) for NVIDIA Nemotron 3 Ultra 550B A55B NVFP4.

### Can I fine-tune Gemini 3.7 Flash or NVIDIA Nemotron 3 Ultra 550B A55B NVFP4?

No. Both are closed models served over API. If you want a model you can train and own, start from an open-weights base in the catalog.

---

[HTML page](https://allocate.network/compare/google-gemini-3-7-flash-vs-nvidia-nemotron-3-ultra-550b-a55b) · [Gemini 3.7 Flash](https://allocate.network/models/google-gemini-3-7-flash.md) · [NVIDIA Nemotron 3 Ultra 550B A55B NVFP4](https://allocate.network/models/nvidia-nemotron-3-ultra-550b-a55b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)