Claude 3 Opus vs Llama 3.1 70B
Head-to-head specifications
| Metric | Claude 3 Opus | Llama 3.1 70B | Difference |
|---|---|---|---|
| MMLU (general capability) | 86.8% | 86.0% | +0.8% |
| Context window | 200K tokens | 128K tokens | — |
| Price (input / output per 1M) | $15 / $75 | Open weights | — |
| Access | Proprietary API | Open weights | — |
- Claude 3 Opus leads general capability (MMLU 86.8% vs 86.0%).
- Claude 3 Opus offers the larger context window, useful for long documents and codebases.
Verdict: Claude 3 Opus or Llama 3.1 70B?
Claude 3 Opus advantages
- Context window (+36%)
Llama 3.1 70B advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Claude 3 Opus if you work with long documents or large codebases.
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.
Claude 3 Opus vs Llama 3.1 70B: which should you choose?
Claude 3 Opus — Anthropic large language model (2024) with a 200K-token context window and an MMLU score of 86.8%.
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.
Claude 3 Opus vs Llama 3.1 70B: Claude 3 Opus scores higher on the MMLU benchmark. Claude 3 Opus leads general capability (MMLU 86.8% vs 86.0%). Claude 3 Opus offers the larger context window, useful for long documents and codebases.
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the Claude 3 Opus scores 86.8% versus 86.0%. 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 Claude 3 Opus handles up to 200K 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
Claude 3 Opus 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 Claude 3 Opus better than the Llama 3.1 70B?
Claude 3 Opus is the clearly stronger overall choice, winning most of the dimensions that matter. Claude 3 Opus leads general capability (MMLU 86.8% vs 86.0%).
What is the main difference between the Claude 3 Opus and the Llama 3.1 70B?
Claude 3 Opus leads general capability (MMLU 86.8% vs 86.0%). Claude 3 Opus offers the larger context window, useful for long documents and codebases.
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 Claude 3 Opus if you work with long documents or large codebases.
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.