GPT-4o mini vs Llama 3.1 70B
Head-to-head specifications
| Metric | GPT-4o mini | Llama 3.1 70B | Difference |
|---|---|---|---|
| MMLU (general capability) | 82.0% | 86.0% | -4.0% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | $0.15 / $0.6 | Open weights | — |
| Access | Proprietary API | Open weights | — |
- Llama 3.1 70B leads general capability (MMLU 86.0% vs 82.0%).
Verdict: GPT-4o mini or Llama 3.1 70B?
GPT-4o mini advantages
- No decisive advantage on the tracked metrics.
Llama 3.1 70B advantages
- General capability (+5%)
Which should you choose?
- Choose the Llama 3.1 70B if you need the strongest reasoning and accuracy.
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.
GPT-4o mini vs Llama 3.1 70B: which should you choose?
GPT-4o mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 82.0%.
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.
GPT-4o 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 82.0%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the Llama 3.1 70B scores 86.0% versus 82.0%. 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 GPT-4o 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
GPT-4o 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 GPT-4o mini better than the Llama 3.1 70B?
Llama 3.1 70B is the clearly stronger overall choice, winning most of the dimensions that matter. Llama 3.1 70B leads general capability (MMLU 86.0% vs 82.0%).
What is the main difference between the GPT-4o mini and the Llama 3.1 70B?
Llama 3.1 70B leads general capability (MMLU 86.0% vs 82.0%).
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.
Which should I choose?
Choose the Llama 3.1 70B if you need the strongest reasoning and accuracy.
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.