GPT-5 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •GPT-5 is meaningfully stronger at math (92 vs 70 on our capability index).
- •Llama 4 is meaningfully stronger at long context (95 vs 80).
- •Llama 4 is open-weights (free to self-host); GPT-5 is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 400,000 tokens (128,000 max output) - better for whole-repo or long-document work.
Specs comparison
| GPT-5 | Llama 4 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Type | Closed source | Open source |
| Context window | 400,000 tokens (128,000 max output) | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | $1.25 | ✓Free (self-host) |
| Output / 1M tokens | $10.00 | Free (self-host) |
| Release date | 2025-08 | 2025-04 |
Benchmarks
| Benchmark | GPT-5 | Llama 4 |
|---|---|---|
| SWE-bench Verified | 74.9% | - |
| AIME 2025 | 94.6% | - |
| GPQA (GPT-5 pro) | 88.4% | - |
| 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 if...
- →You want strong reasoning at the lowest frontier-model price
- →Existing GPT-5-based systems that are already tuned and validated
- →General coding, math, and reasoning workloads on a budget
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