NVIDIA Nemotron 3 Nano 30B A3B BF16 vs GPT-5 Nano
At a Glance
| Compare |  GPT-5 NanoOpenAI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #46 of 460.0 score · 2/3 sources · provisional · missing LiveBench · full-core range 0.0–33.3 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.050Openrouter ↗ · Aug 29, 2026 | $0.025Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.20Openrouter ↗ · Aug 29, 2026 | $0.20Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 400K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | GPT-5 Nano |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,348.44100% of row best · rating · nvidia-nemotron-3-nano-30b-a3b-bf16; 95% CI [1342.98546755, 1353.88978012]; votes 15325; rank 195 | 1,319.7298% of row best · rating · gpt-5-nano-high; 95% CI [1312.87607943, 1326.57003978]; votes 8128; rank 218 |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | GPT-5 Nano |
|---|---|---|
| Developer | NVIDIA | OpenAI |
| Family | Nvidia Nemotron 3 Nano 30b A3b Bf16 | Gpt 5 |
| Model | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | GPT-5 Nano |
| Version | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | GPT-5 Nano |
| Lifecycle | active | active |
| Released | 2025-12-15 | 2025-08-07 |
| Knowledge cutoff | 2025-11-28 | 2024-05-31 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 400K |
| Total parameters | 31.6B | Unknown |
| Active parameters | 3.5B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, structured_outputs, tools |
NVIDIA Nemotron 3 Nano 30B A3B BF16 Capabilities
GPT-5 Nano Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs | GPT-6 AstraOpenAI | cross-developer peerstext | |
Claude Fable 5.1Anthropic | vs | cross-developer peerstext | |
 Gemini 3.1 ProGoogle DeepMind | vs | cross-developer peerstext | |
 DeepSeek-V4.1-FlashDeepSeek | vs | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | cross-developer peerstext | |
 Kimi-K3Moonshot AI | vs | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs |  GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
Seed 2.1 ProByteDance Seed | vs | cross-developer peerstext | |
 Mistral Large 3Mistral AI | vs | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
NVIDIA Nemotron 3 Nano 30B A3B BF16 vs GPT-5 Nano FAQs
Is NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both NVIDIA Nemotron 3 Nano 30B A3B BF16 and GPT-5 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
NVIDIA Nemotron 3 Nano 30B A3B BF16 is $0.050 and GPT-5 Nano is $0.025 per million tokens, so GPT-5 Nano is cheaper on this metric. NVIDIA Nemotron 3 Nano 30B A3B BF16 is $0.20 and GPT-5 Nano is $0.20 per million tokens, so they are tied on this metric.
Which has a larger context window, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
GPT-5 Nano has the larger sourced context window. NVIDIA Nemotron 3 Nano 30B A3B BF16 supports 262K and GPT-5 Nano supports 400K.
Which performs better in benchmarks, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano be self-hosted?+
NVIDIA Nemotron 3 Nano 30B A3B BF16 is the only model in this pair currently marked as self-hostable. NVIDIA Nemotron 3 Nano 30B A3B BF16 is open weight; GPT-5 Nano is not marked open weight.
Can NVIDIA Nemotron 3 Nano 30B A3B BF16 and GPT-5 Nano understand images?+
NVIDIA Nemotron 3 Nano 30B A3B BF16 is not documented with image input; GPT-5 Nano is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
NVIDIA Nemotron 3 Nano 30B A3B BF16 has the larger sourced maximum output: NVIDIA Nemotron 3 Nano 30B A3B BF16 supports 1,049K and GPT-5 Nano supports 128K output tokens.
Do NVIDIA Nemotron 3 Nano 30B A3B BF16 and GPT-5 Nano support reasoning and tool use?+
NVIDIA Nemotron 3 Nano 30B A3B BF16: reasoning and tool calling. GPT-5 Nano: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
NVIDIA Nemotron 3 Nano 30B A3B BF16 has 3 sourced provider routes; GPT-5 Nano has 2, so NVIDIA Nemotron 3 Nano 30B A3B BF16 has broader tracked availability.
Which offers better value, NVIDIA Nemotron 3 Nano 30B A3B BF16 or GPT-5 Nano?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.