API cost decision in 10 seconds

gpt-oss-safeguard-20b vs Llama Guard 4 12B

Pick gpt-oss-safeguard-20b for lower cost; pick Llama Guard 4 12B only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick gpt-oss-safeguard-20b for lower cost; pick Llama Guard 4 12B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, gpt-oss-safeguard-20b is estimated at $0.22 vs $0.27 for Llama Guard 4 12B, saving $0.05 (16.7% lower).

Cost-first pickgpt-oss-safeguard-20b
Context-first pickLlama Guard 4 12B
Sample savings$0.0516.7%
10x traffic gap$0.45

Llama Guard 4 12B has more context, but gpt-oss-safeguard-20b saves $0.05 on the standard workload. At 10x that traffic, the same price gap is about $0.45. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors gpt-oss-safeguard-20b, balanced workload favors Llama Guard 4 12B, and output-heavy chatbot favors Llama Guard 4 12B.

Workload shapeToken mixBetter pickgpt-oss-safeguard-20bLlama Guard 4 12B
Input-heavy / RAG5M input + 500K outputgpt-oss-safeguard-20b$0.53$0.99
Balanced workload1M input + 1M outputLlama Guard 4 12B$0.38$0.36
Output-heavy chatbot1M input + 5M outputLlama Guard 4 12B$1.57$1.08
Cheaper input gpt-oss-safeguard-20b $0.075 vs $0.18 / 1M

gpt-oss-safeguard-20b is $0.1 cheaper per 1M input tokens (58.3% lower; 2.4x difference).

Cheaper output Llama Guard 4 12B $0.3 vs $0.18 / 1M

Llama Guard 4 12B is $0.12 cheaper per 1M output tokens (40% lower; 1.67x difference).

Larger context Llama Guard 4 12B 131.07K vs 163.84K

Llama Guard 4 12B has 32.77K more context (1.25x larger).

Sample workload gpt-oss-safeguard-20b $0.22 vs $0.27

gpt-oss-safeguard-20b is $0.05 cheaper on the standard workload (16.7% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
gpt-oss-safeguard-20b Calculating… Estimated API cost
Llama Guard 4 12B 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

gpt-oss-safeguard-20b has the lower input price; Llama Guard 4 12B has the lower output price; Llama Guard 4 12B offers the larger context window. For the 1M input plus 500K output sample, gpt-oss-safeguard-20b is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.22 for gpt-oss-safeguard-20b and $0.27 for Llama Guard 4 12B.

Best Fit

Choose gpt-oss-safeguard-20b when you care most about lower input-token price.

Choose Llama Guard 4 12B when you care most about lower output-token price, and larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, gpt-oss-safeguard-20b is estimated at $0.22 vs $0.27 for Llama Guard 4 12B, saving $0.05 (16.7% lower).
  • gpt-oss-safeguard-20b is $0.05 cheaper on the standard workload (16.7% lower).
  • gpt-oss-safeguard-20b is $0.1 cheaper per 1M input tokens (58.3% lower; 2.4x difference).
  • Llama Guard 4 12B is $0.12 cheaper per 1M output tokens (40% lower; 1.67x difference).
  • Llama Guard 4 12B has 32.77K more context (1.25x larger).
Head-to-Head Specs
Featuregpt-oss-safeguard-20b
(OpenAI)
Llama Guard 4 12B
(Meta)
Input Price
prompt tokens per 1M
$0.075$0.18
Completion Price
per 1M tokens
$0.3$0.18
Sample Workload Cost
1M input + 500K output
$0.22$0.27
Context Window131.07K163.84K
Release Date
Popularity#118#146

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productiongpt-oss-safeguard-20bOn the standard 1M input plus 500K output workload, gpt-oss-safeguard-20b is estimated at $0.22 vs $0.27 for Llama Guard 4 12B, saving $0.05 (16.7% lower).
High-volume input processinggpt-oss-safeguard-20bLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLlama Guard 4 12BLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workLlama Guard 4 12BA 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.
  • MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.
  • DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

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OpenAI catalog

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Meta catalog

Check other Meta models with comparable pricing, context, or release timing.

Open Meta models
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...

Llama Guard 4 12B

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...