GPT-oss 120B vs Llama 4 Scout — Pricing Comparison 2026

Requests per Day

Days per Month

OpenAI

GPT-oss 120B

$0.00

per month

Input cost

Output cost

Cost per request

Requests/month

Meta (Together.ai)

Llama 4 Scout

$0.00

per month

Input cost

Output cost

Cost per request

Requests/month

Which Model for Which Use Case?

Cost Optimization

Both models are priced within 2% of each other — among the cheapest AI models available. GPT-oss 120B edges out a 17% advantage on input tokens, making it slightly better for input-heavy workloads. The difference is marginal at budget pricing.

Input-heavy workloads: GPT-oss 120B (17% cheaper input)

Long-Document Processing

Llama 4 Scout has a 1M token context window — 7.8x larger than GPT-oss 120B's 128K. For full books, large codebases, or extensive analysis, Llama 4 Scout is the clear choice. You can process more data in a single prompt, reducing total API calls.

Long context: Llama 4 Scout (1M vs 128K)

Self-Hosting & Flexibility

Both models are open-source/open-weights and available via Together.ai. Both can be self-hosted on your own infrastructure, eliminating API costs entirely. Choose based on your hardware capabilities and context window needs.

Self-host: Either works | Large context self-host: Llama 4 Scout

High-Volume Chatbot & Coding

For high-volume chatbot or coding workloads with moderate context, GPT-oss 120B offers slightly lower input costs. Both handle coding tasks well. For chatbots that accumulate long conversation history, Llama 4 Scout's 1M context prevents truncation issues.

Short-context high volume: GPT-oss 120B | Long conversations: Llama 4 Scout

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Frequently Asked Questions

Is GPT-oss 120B cheaper than Llama 4 Scout?

GPT-oss 120B is slightly cheaper on input tokens. GPT-oss 120B costs $0.15/M input and $0.60/M output. Llama 4 Scout costs $0.18/M input and $0.59/M output. GPT-oss is 17% cheaper on input, while Llama 4 Scout is 2% cheaper on output. For a typical workload of 1M input + 500K output tokens/month, GPT-oss 120B costs $0.45 vs Llama 4 Scout's $0.475 — a negligible $0.025 difference.

What is the biggest difference between GPT-oss 120B and Llama 4 Scout?

The biggest difference is context window size. Llama 4 Scout has a 1M token context window — 7.8x larger than GPT-oss 120B's 128K context. This matters significantly for use cases involving long documents, large codebases, or extensive conversation histories. Both models are open-source and priced similarly, so context window is the primary differentiator.

When should I choose Llama 4 Scout over GPT-oss 120B?

Choose Llama 4 Scout when you need: (1) long-context processing (1M tokens vs 128K), (2) analyzing full books, codebases, or extensive documents in a single prompt, (3) complex multi-turn conversations that accumulate large context. Choose GPT-oss 120B when input token volume is high and you want to minimize input costs, or when your tasks fit comfortably within 128K context.

Are both GPT-oss 120B and Llama 4 Scout open source?

Yes, both are open-weight/open-source models. GPT-oss 120B is OpenAI's open-source offering, and Llama 4 Scout is Meta's latest open-weight model. Both are available via the Together.ai API and can be self-hosted. This makes them excellent choices for teams that want flexibility, transparency, and the option to run models on their own infrastructure to eliminate API costs entirely.