OpenAI o1-mini vs Llama 3.1 70B
Head-to-head specifications
| Metric | OpenAI o1-mini | Llama 3.1 70B | Difference |
|---|---|---|---|
| MMLU (general capability) | 85.2% | 86.0% | -0.8% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | $3 / $12 | Open weights | — |
| Access | Proprietary API | Open weights | — |
- Llama 3.1 70B leads general capability (MMLU 86.0% vs 85.2%).
Verdict: OpenAI o1-mini or Llama 3.1 70B?
OpenAI o1-mini advantages
- No decisive advantage on the tracked metrics.
Llama 3.1 70B advantages
- No decisive advantage on the tracked metrics.
Value for money
Llama 3.1 70B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
OpenAI o1-mini vs Llama 3.1 70B: which should you choose?
OpenAI o1-mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 85.2%.
Llama 3.1 70B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 86.0%, released with open weights.
OpenAI o1-mini vs Llama 3.1 70B: Llama 3.1 70B scores higher on the MMLU benchmark. Llama 3.1 70B leads general capability (MMLU 86.0% vs 85.2%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the Llama 3.1 70B scores 86.0% versus 85.2%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The OpenAI o1-mini handles up to 128K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
OpenAI o1-mini is proprietary api and Llama 3.1 70B is open weights. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the OpenAI o1-mini better than the Llama 3.1 70B?
On paper these are near-identical; decide on price, availability or brand preference. Llama 3.1 70B leads general capability (MMLU 86.0% vs 85.2%).
What is the main difference between the OpenAI o1-mini and the Llama 3.1 70B?
Llama 3.1 70B leads general capability (MMLU 86.0% vs 85.2%).
Which is better value?
Llama 3.1 70B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.