API cost decision in 10 seconds

Nemotron Nano 9B V2 (free) vs gpt-oss-safeguard-20b

Pick Nemotron Nano 9B V2 (free) for lower cost; pick gpt-oss-safeguard-20b only if the larger context window matters more.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T19:08:13Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Nemotron Nano 9B V2 (free) for lower cost; pick gpt-oss-safeguard-20b only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Nemotron Nano 9B V2 (free) is estimated at $0 vs $0.22 for gpt-oss-safeguard-20b, saving $0.22 (100% lower).

Cost-first pickNemotron Nano 9B V2 (free)

Context-first pickgpt-oss-safeguard-20b

Sample savings$0.22100%

10x traffic gap$2.25

gpt-oss-safeguard-20b has more context, but Nemotron Nano 9B V2 (free) saves $0.22 on the standard workload. At 10x that traffic, the same price gap is about $2.25. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Nemotron Nano 9B V2 (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Nemotron Nano 9B V2 (free)	gpt-oss-safeguard-20b
Input-heavy / RAG	5M input + 500K output	Nemotron Nano 9B V2 (free)	$0	$0.53
Balanced workload	1M input + 1M output	Nemotron Nano 9B V2 (free)	$0	$0.38
Output-heavy chatbot	1M input + 5M output	Nemotron Nano 9B V2 (free)	$0	$1.57

Cheaper input Nemotron Nano 9B V2 (free) $0 vs $0.075 / 1M

Nemotron Nano 9B V2 (free) is free for input tokens while gpt-oss-safeguard-20b costs $0.07 per 1M tokens.

Cheaper output Nemotron Nano 9B V2 (free) $0 vs $0.3 / 1M

Nemotron Nano 9B V2 (free) is free for output tokens while gpt-oss-safeguard-20b costs $0.3 per 1M tokens.

Larger context gpt-oss-safeguard-20b 128K vs 131.07K

gpt-oss-safeguard-20b has 3.07K more context (1.02x larger).

Sample workload Nemotron Nano 9B V2 (free) $0 vs $0.22

Nemotron Nano 9B V2 (free) is free for the standard workload while the other model is estimated at $0.22.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Nemotron Nano 9B V2 (free) Calculating… Estimated API cost

gpt-oss-safeguard-20b Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Nemotron Nano 9B V2 (free) has the lower input price; Nemotron Nano 9B V2 (free) has the lower output price; gpt-oss-safeguard-20b offers the larger context window. For the 1M input plus 500K output sample, Nemotron Nano 9B V2 (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Nemotron Nano 9B V2 (free) and $0.22 for gpt-oss-safeguard-20b.

Best Fit

Choose Nemotron Nano 9B V2 (free) when you care most about lower input-token price, and lower output-token price.

Choose gpt-oss-safeguard-20b when you care most about larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Nemotron Nano 9B V2 (free) is estimated at $0 vs $0.22 for gpt-oss-safeguard-20b, saving $0.22 (100% lower).
Nemotron Nano 9B V2 (free) is free for the standard workload while the other model is estimated at $0.22.
Nemotron Nano 9B V2 (free) is free for input tokens while gpt-oss-safeguard-20b costs $0.07 per 1M tokens.
Nemotron Nano 9B V2 (free) is free for output tokens while gpt-oss-safeguard-20b costs $0.3 per 1M tokens.
gpt-oss-safeguard-20b has 3.07K more context (1.02x larger).

Head-to-Head Specs

Feature	Nemotron Nano 9B V2 (free) (NVIDIA)	gpt-oss-safeguard-20b (OpenAI)
Input Price prompt tokens per 1M	$0	$0.075
Completion Price per 1M tokens	$0	$0.3
Sample Workload Cost 1M input + 500K output	$0	$0.22
Context Window	128K	131.07K
Release Date	2025-09-05	2025-10-29
Popularity	#107	#124

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Nemotron Nano 9B V2 (free)	On the standard 1M input plus 500K output workload, Nemotron Nano 9B V2 (free) is estimated at $0 vs $0.22 for gpt-oss-safeguard-20b, saving $0.22 (100% lower).
High-volume input processing	Nemotron Nano 9B V2 (free)	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Nemotron Nano 9B V2 (free)	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	gpt-oss-safeguard-20b	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

gpt-oss-120b (free) can replace gpt-oss-safeguard-20b when lower sample workload cost matters most: $0.
gpt-oss-20b (free) can replace gpt-oss-safeguard-20b when lower sample workload cost matters most: $0.
gpt-oss-20b can replace gpt-oss-safeguard-20b when lower sample workload cost matters most: $0.1.
gpt-oss-120b can replace gpt-oss-safeguard-20b when lower sample workload cost matters most: $0.13.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.

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Provider catalogs

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NVIDIA catalog

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OpenAI catalog

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Open OpenAI models

Nemotron Nano 9B V2 (free)

NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and...

gpt-oss-safeguard-20b

gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...