GPT-5.5 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •GPT-5.5 is meaningfully stronger at coding (93 vs 72 on our capability index).
- •Llama 4 is meaningfully stronger at cost efficiency (82 vs 58).
- •Llama 4 is open-weights (free to self-host); GPT-5.5 is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 1,050,000 tokens (128,000 max output) - better for whole-repo or long-document work.
Specs comparison
| GPT-5.5 | Llama 4 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Type | Closed source | Open source |
| Context window | 1,050,000 tokens (128,000 max output) | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | $5.00 | ✓Free (self-host) |
| Output / 1M tokens | $30.00 | Free (self-host) |
| Release date | 2026-04 | 2025-04 |
Benchmarks
| Benchmark | GPT-5.5 | Llama 4 |
|---|---|---|
| SWE-bench Verified | 82.6% | - |
| SWE-bench Pro | 58.6% | - |
| Scout context window | - | 10M tokens |
| Scout size | - | 17B active / 109B total (16 experts) |
| Maverick size | - | 17B active / 400B total (128 experts) |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose GPT-5.5 if...
- →Complex professional coding, data analysis, and multi-tool agentic workflows
- →Long-document or large-codebase tasks needing a 1M+ context window
- →You want OpenAI's recommended default frontier model for new projects
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model