Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

GPT-4o mini vs NVIDIA: Nemotron 3.5 Content Safety (free) benchmark

Name: GPT-4o mini vs NVIDIA: Nemotron 3.5 Content Safety (free) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GPT-4o mini (OpenAI) and NVIDIA: Nemotron 3.5 Content Safety (free) (nvidia) using the latest snapshots we have as of Aug 16, 2026. NVIDIA: Nemotron 3.5 Content Safety (free) wins on output price at $0/1M. GPT-4o mini has the larger context window (128k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

OpenAI

GPT-4o mini

Legacy small OpenAI model. Useful as a cheap baseline.

Elo 1,272$0.6/1M out

nvidia

NVIDIA: Nemotron 3.5 Content Safety (free)

Auto-discovered from OpenRouter (nvidia/nemotron-3.5-content-safety:free). Preview until a second source matches.

$0/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-4o mini (6)vsNVIDIA: Nemotron 3.5 Content Safety (free) (2)

GPT-4o mini: 6W (75%)Overall: GPT-4o miniNVIDIA: Nemotron 3.5 Content Safety (free): 2W (25%)

← GPT-4o mini1 tiesNVIDIA: Nemotron 3.5 Content Safety (free) →

GPT-4o mini (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,272vs—

Coding Elo

—vs—

SWE-bench

41.2%vs—

LiveBench

48.1%vs—

GPQA Diamond

60.1%vs—

GPT-4o mini: 4WNVIDIA: Nemotron 3.5 Content Safety (free): 0W

GPT-4o mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

160 tok/svs—

Time to first token

130 msvs—

GPT-4o mini: 2WNVIDIA: Nemotron 3.5 Content Safety (free): 0W

NVIDIA: Nemotron 3.5 Content Safety (free) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.6/1Mvs$0/1M

Input price

$0.15/1Mvs$0/1M

Context window

128kvs128k

GPT-4o mini: 0WNVIDIA: Nemotron 3.5 Content Safety (free): 2W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGPT-4o mini 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

NVIDIA: Nemotron 3.5 Content Safety (free)

$0.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,272

EloHigh is better

—

LiveBench

High is better

48.1%

LiveBenchHigh is better

—

SWE-bench

High is better

41.2%

SWE-benchHigh is better

—

GPQA

High is better

60.1%

GPQAHigh is better

—

TTFT

Low is better

130 ms

TTFTLow is better

—

Speed

High is better

160 tok/s

SpeedHigh is better

—

In $

Low is better

$0.15/1M

In $Low is better

$0/1M

Out $

Low is better

$0.6/1M

Out $Low is better

$0/1M

Context

High is better

128k

ContextHigh is better

128k

Key Selection Recommendations

1
NVIDIA: Nemotron 3.5 Content Safety (free):Pick NVIDIA: Nemotron 3.5 Content Safety (free) when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatNVIDIA: Nemotron 3.5 Content Safety (free)

Lower output list price ($0/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGPT-4o mini

Larger window (128k).

Screenshots / visionGPT-4o mini

GPT-4o mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	NVIDIA: Nemotron 3.5 Content Safety (free)	Lower output list price ($0/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GPT-4o mini	Larger window (128k).
Screenshots / vision	GPT-4o mini	GPT-4o mini is the side marked multimodal in the catalog.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/gpt-4o-mini-vs-nemotron-3-5-content-safety-free/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

OpenAI

GPT-4o mini

Elo 1,272

LiveBench48.1%

SWE-bench41.2%

Speed160 tok/s

Output cost$0.6/1M

nvidia

NVIDIA: Nemotron 3.5 Content Safety (free)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0/1M

Frequently Asked Questions

Which is better overall, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: NVIDIA: Nemotron 3.5 Content Safety (free) output tokens are $0/1M versus $0.6/1M. Input prices and retry rates still move the real bill.
Which is faster, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?: We do not have TTFT for both models.
Which has the larger context window?: GPT-4o mini accepts 128k tokens versus 128k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GPT-4o mini and NVIDIA: Nemotron 3.5 Content Safety (free) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GPT-4o mini matchups →|All NVIDIA: Nemotron 3.5 Content Safety (free) matchups →

GPT-4o mini vs OpenAI o1-mini (next gpt-mini)GPT-5 vs GPT-4o mini GPT-4.5 Orion vs GPT-4o mini GPT-5.6 Sol vs GPT-4o mini GPT-5.6 Terra vs GPT-4o mini GPT-4 Turbo vs GPT-4o mini

GPT-4o mini vs NVIDIA: Nemotron 3.5 Content Safety (free) benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-4o mini (6)vsNVIDIA: Nemotron 3.5 Content Safety (free) (2)

GPT-4o mini: 6W (75%)Overall: GPT-4o miniNVIDIA: Nemotron 3.5 Content Safety (free): 2W (25%)

← GPT-4o mini1 tiesNVIDIA: Nemotron 3.5 Content Safety (free) →

GPT-4o mini (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,272vs—

Coding Elo

—vs—

SWE-bench

41.2%vs—

LiveBench

48.1%vs—

GPQA Diamond

60.1%vs—

GPT-4o mini: 4WNVIDIA: Nemotron 3.5 Content Safety (free): 0W

GPT-4o mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

160 tok/svs—

Time to first token

130 msvs—

GPT-4o mini: 2WNVIDIA: Nemotron 3.5 Content Safety (free): 0W

NVIDIA: Nemotron 3.5 Content Safety (free) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.6/1Mvs$0/1M

Input price

$0.15/1Mvs$0/1M

Context window

128kvs128k

GPT-4o mini: 0WNVIDIA: Nemotron 3.5 Content Safety (free): 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatNVIDIA: Nemotron 3.5 Content Safety (free)

Lower output list price ($0/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGPT-4o mini

Larger window (128k).

Screenshots / visionGPT-4o mini

GPT-4o mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	NVIDIA: Nemotron 3.5 Content Safety (free)	Lower output list price ($0/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GPT-4o mini	Larger window (128k).
Screenshots / vision	GPT-4o mini	GPT-4o mini is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

NVIDIA: Nemotron 3.5 Content Safety (free) output tokens are $0/1M versus $0.6/1M. Input prices and retry rates still move the real bill.

Which is faster, GPT-4o mini or NVIDIA: Nemotron 3.5 Content Safety (free)?

We do not have TTFT for both models.

Which has the larger context window?

GPT-4o mini accepts 128k tokens versus 128k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GPT-4o mini and NVIDIA: Nemotron 3.5 Content Safety (free) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.