GPT-oss vs Llama 4
The open-source LLM showdown. GPT-oss offers OpenAI-quality models at budget prices, while Llama 4 brings massive context windows and the Meta ecosystem. Both are self-hostable.
Pricing data verified: Jun 7, 2026
Full Model Lineup
| Specification | GPT-oss 120B (OpenAI) | Llama 4 Scout (Meta) |
|---|---|---|
| Input Price (per 1M tokens) | $0.15 | $0.11 |
| Output Price (per 1M tokens) | $0.60 | $0.34 |
| Context Window | 128K tokens | 10M tokens |
| Tier | Budget | Budget |
| License | Open-source | Open-source (Apache 2.0) |
| Self-Hostable | Yes (~4x A100 80GB) | Yes (~1x H100 80GB) |
| API Provider | OpenAI / Together.ai | Together.ai |
Calculate Your Exact Costs
Compare GPT-oss 120B vs Llama 4 Scout at your actual usage level.
Which Model for Which Use Case?
Long-Context Document Analysis
Llama 4 Scout's 10M context window lets you process entire codebases, legal documents, or multi-hour transcripts in a single pass — no chunking required.
Complex Reasoning & Code
GPT-oss 120B's training on OpenAI methodology gives it an edge on multi-step reasoning, code generation, and instruction following.
High-Volume Chatbots
Both are affordable, but Llama 4 Scout is 43% cheaper on output ($0.34 vs $0.60) — making it the better choice for conversational workloads at scale.
Self-Hosting at Scale
GPT-oss 20B runs on a single GPU, while Llama 4 Scout needs an H100. For budget self-hosting with moderate quality, GPT-oss 20B wins.
Comparing open-source API costs?
APIpulse Pro lets you compare all 39 models, find the cheapest option for your exact usage, and save scenarios for your team.
Frequently Asked Questions
Is GPT-oss or Llama 4 cheaper?
It depends on the model size. GPT-oss 20B is the cheapest at $0.08/M input and $0.35/M output. Llama 4 Scout costs $0.11/M input and $0.34/M output. At the 120B tier, GPT-oss 120B is 25% cheaper on input ($0.15 vs $0.20) with identical output pricing to Llama 4 Maverick.
What is the context window difference?
GPT-oss models have 128K token context windows. Llama 4 Scout has 10M tokens — 78x larger. Llama 4 Maverick has 1M tokens (8x larger). Llama 4's context advantage is massive for document-heavy workloads.
Can I self-host both?
Yes, both are open-source. GPT-oss 20B needs ~1x A100 80GB; 120B needs ~4x A100s. Llama 4 Scout needs ~1x H100 80GB; Maverick needs 2-4x H100s. Self-hosting eliminates per-token costs but requires GPU infrastructure.
Which has better quality?
GPT-oss 120B generally outperforms Llama 4 Scout on reasoning, code generation, and instruction following. Llama 4 Scout excels at long-context tasks thanks to its 10M window. Maverick is competitive with GPT-oss 120B on most benchmarks.