Llama 3.1 8B vs GPT-4o mini
Head-to-head specifications
| Metric | Llama 3.1 8B | GPT-4o mini | Difference |
|---|---|---|---|
| MMLU (general capability) | 69.4% | 82.0% | -12.6% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | Open weights | $0.15 / $0.6 | — |
| Access | Open weights | Proprietary API | — |
- GPT-4o mini leads general capability (MMLU 82.0% vs 69.4%).
Verdict: Llama 3.1 8B or GPT-4o mini?
Llama 3.1 8B advantages
- No decisive advantage on the tracked metrics.
GPT-4o mini advantages
- General capability (+15%)
Which should you choose?
- Choose the GPT-4o mini if you need the strongest reasoning and accuracy.
Value for money
Llama 3.1 8B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Llama 3.1 8B vs GPT-4o mini: which should you choose?
Llama 3.1 8B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 69.4%, released with open weights.
GPT-4o mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 82.0%.
Llama 3.1 8B vs GPT-4o mini: GPT-4o mini scores higher on the MMLU benchmark. GPT-4o mini leads general capability (MMLU 82.0% vs 69.4%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the GPT-4o mini scores 82.0% versus 69.4%. 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 Llama 3.1 8B 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
Llama 3.1 8B is open weights and GPT-4o mini is proprietary api. 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 Llama 3.1 8B better than the GPT-4o mini?
GPT-4o mini is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-4o mini leads general capability (MMLU 82.0% vs 69.4%).
What is the main difference between the Llama 3.1 8B and the GPT-4o mini?
GPT-4o mini leads general capability (MMLU 82.0% vs 69.4%).
Which is better value?
Llama 3.1 8B 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 GPT-4o mini 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.