Llama 4 vs o1
2026 - Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is open-weights - free to self-host with no API costs. o1 requires paid API access.
- •Llama 4 has a 10M context window - 50x larger than o1's 200K. Better for long documents and large codebases.
- •Llama 4 is open-source: fine-tune it, self-host it, or use any inference provider. o1 is closed-source.
Specs comparison
| Llama 4 | o1 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Type | Open source | Closed source |
| Context window | ✓10M | 200K |
| Input / 1M tokens | ✓Free (self-host) | $15.00 |
| Output / 1M tokens | Free (self-host) | $60.00 |
| Release date | 2025-04 | 2024-09 |
Benchmarks
| Benchmark | Llama 4 | o1 |
|---|---|---|
| MMLU | ~85% | - |
| GPQA Diamond | - | 78.3% |
| HumanEval | - | 92.4% |
| SWE-bench Verified | - | 48.9% |
Scores sourced from official provider release posts.
Strengths
Llama 4
- ✓Fully open weights - no usage restrictions
- ✓10M context in Llama 4 Scout variant
- ✓Native multimodal support
- ✓Strong performance relative to size
- ✓Enormous ecosystem of community tools and fine-tunes
o1
- ✓Best-in-class math and physics
- ✓Strong competitive coding (Codeforces, HumanEval)
- ✓Scientific reasoning (GPQA top performer)
- ✓Multi-step logic and planning
- ✓200K context for long technical documents
Which should you choose?
Choose Llama 4 if you need...
- →Self-hosted and on-premise deployments
- →Privacy-sensitive workloads
- →Custom fine-tuning
- →Researchers and open-source builders
Choose o1 if you need...
- →Math and science problems
- →Competitive programming
- →Complex multi-step reasoning
- →Research assistance