Comparisons

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.

Gemini 3.7 Flash NVIDIA Nemotron 3 Ultra 550B A55B NVFP4
LabGoogleNVIDIA
AccessAPI onlyAPI only
Context window1M tokens512K tokens
List price, input$0.75 / M tokens$0.6 / M tokens
List price, output$3.75 / M tokens$3.6 / M tokens
Cached inputn/a$0.2 / M tokens
LicenseProprietary APIProprietary API
Fine-tunableNoNo

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.

NVIDIA Nemotron 3 Ultra 550B A55B NVFP4$0.60$3.60
Gemini 3.7 Flash$0.75$3.75
InputOutput

Choose Gemini 3.7 Flash for

  • The longer context window (1M vs 512K tokens)
Gemini 3.7 Flash details

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)
NVIDIA Nemotron 3 Ultra 550B A55B NVFP4 details

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.

Related comparisons

Run the numbers on your workload

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.