GPT-5.6 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •GPT-5.6 is meaningfully stronger at coding (94 vs 72 on our capability index).
- •Llama 4 is meaningfully stronger at cost efficiency (82 vs 70).
- •Llama 4 is open-weights (free to self-host); GPT-5.6 is paid API only.
Specs comparison
| GPT-5.6 | Llama 4 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Type | Closed source | Open source |
| Context window | Not announced | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | 5 | ✓Free (self-host) |
| Output / 1M tokens | 30 | Free (self-host) |
| Release date | 2026-06 | 2025-04 |
Benchmarks
| Benchmark | GPT-5.6 | Llama 4 |
|---|---|---|
| Terminal-Bench 2.1 (Sol) | 88.8% | - |
| Terminal-Bench 2.1 (Terra) | 82.5% | - |
| Terminal-Bench 2.1 (Luna) | 84.3% | - |
| Terminal-Bench 2.1 | 88.8 | - |
| ExploitBench | Competitive with Anthropic Mythos Preview | - |
| ExploitGym | Significant improvements | - |
| GeneBench v1 | Not announced | - |
| 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-5.6 if...
- →You want to standardize on one model generation but route requests across cost/quality tiers
- →You are an approved preview partner exploring the newest OpenAI capabilities
- →Workloads spanning agentic coding, knowledge work, and research where tier flexibility helps
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