Llama 4 vs o1
Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is meaningfully stronger at cost efficiency (82 vs 35 on our capability index).
- •o1 is meaningfully stronger at math (88 vs 70).
- •Llama 4 is open-weights (free to self-host); o1 is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 200,000 tokens (100,000 max output) - better for whole-repo or long-document work.
Specs comparison
| Llama 4 | o1 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Type | Open source | Closed source |
| Context window | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) | 200,000 tokens (100,000 max output) |
| Input / 1M tokens | ✓Free (self-host) | $15.00 |
| Output / 1M tokens | Free (self-host) | $60.00 |
| Release date | 2025-04 | 2024-12 |
Benchmarks
| Benchmark | Llama 4 | o1 |
|---|---|---|
| Scout context window | 10M tokens | - |
| Scout size | 17B active / 109B total (16 experts) | - |
| Maverick size | 17B active / 400B total (128 experts) | - |
| AIME 2024 | - | 74% |
| GPQA Diamond | - | 77.3% |
| Codeforces | - | ~89th percentile |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
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
Choose o1 if...
- →Hard, multi-step math, science, and logic problems that reward deliberate reasoning
- →Competitive programming and algorithmic problem solving
- →Existing o1-based pipelines already validated for reasoning tasks