At this usage level, the cost difference is negligible. The decision comes down to quality and context window, not price.

Mid Tier: GPT-oss 120B vs Llama 4 Maverick

For teams that need higher quality output:

• GPT-oss 120B: $0.15 input / $0.60 output per 1M tokens
• Llama 4 Maverick: $0.20 input / $0.60 output per 1M tokens

GPT-oss 120B is 25% cheaper on input with identical output pricing. For most use cases, GPT-oss 120B offers better value at this tier.

Context Window: Llama 4's Secret Weapon

The biggest differentiator isn't price — it's context window:

• GPT-oss (both sizes): 128K tokens
• Llama 4 Scout: 1M tokens
• Llama 4 Maverick: 1M tokens

Llama 4 Scout (1M context) window is a game-changer for document-heavy workloads. You can process entire codebases, legal document collections, or multi-hour transcripts in a single pass — without chunking. GPT-oss models top out at 128K, which is adequate for most tasks but limits large-scale document analysis.

Quality Comparison

General Reasoning

GPT-oss 120B generally outperforms Llama 4 Scout on reasoning benchmarks. It handles complex multi-step logic, mathematical operations, and nuanced instruction following with fewer errors. Llama 4 Maverick is competitive with GPT-oss 120B on most reasoning tasks.

Code Generation

Both families produce solid code, but with different strengths. GPT-oss 120B generates more idiomatic code with better adherence to conventions. Llama 4 Scout excels at understanding large codebases thanks to its massive context window — you can feed it an entire repository and get coherent refactoring suggestions.

Instruction Following

GPT-oss models follow complex, multi-part instructions more reliably. For structured output pipelines, chain-of-thought workflows, and agent-based systems, GPT-oss 120B is the stronger choice. Llama 4 models sometimes deviate on longer instruction sets.

Long-Context Tasks

This is where Llama 4 shines. Scout's 1M context window means you can analyze massive documents without chunking — a significant engineering advantage. Maverick's 1M context is also substantially larger than GPT-oss's 128K, making both Llama 4 models better for document-heavy workflows.

Cost Scenarios at 3 Scale Levels

Decision Framework

Choose GPT-oss When:

• Input-heavy workloads where the lower input price matters (classification, extraction, routing)
• You need strong instruction following for structured output pipelines
• Code generation quality is a priority
• You want to stay within the OpenAI ecosystem
• 128K context is sufficient for your use case

Choose Llama 4 When:

• You need massive context windows (Scout's 1M tokens) for document analysis
• Long-context understanding is more important than input cost savings
• You prefer Meta's licensing terms for commercial use
• You want the flexibility of Together.ai's infrastructure
• Your workload is output-heavy where Scout's slightly lower output price adds up

Open-source LLM APIs have reached parity with proprietary models for most workloads. The choice between GPT-oss and Llama 4 comes down to context window needs, not price — both are incredibly affordable.

— See if you're overpaying for AI APIs