Capability · Comparison
Llama 3.1 405B vs Llama 3.3 70B
Llama 3.1 405B was Meta's headline 'GPT-4-class' open-weights release of 2024. Llama 3.3 70B came a few months later and quietly delivered near-405B quality at a fraction of the serving cost — a story the frontier community keeps repeating. If you're deciding which Llama to self-host, this is the comparison that matters.
Side-by-side
| Criterion | Llama 3.1 405B | Llama 3.3 70B |
|---|---|---|
| Parameters | 405B | 70B |
| License | Llama Community License | Llama Community License |
| Context window | 128,000 tokens | 128,000 tokens |
| MMLU-Pro / GPQA | Frontier | Near-parity — within 1–3 points typically |
| Coding (HumanEval+) | Very strong | Very close to 405B |
| GPUs to serve (fp8) | 8x H100 minimum | 2x H100 comfortably |
| Inference cost on commodity API | High | Roughly 6–8x cheaper than 405B |
| Best fit | Research, fine-tune donor, benchmarks | Production self-hosted default |
Verdict
For nearly every production workload, Llama 3.3 70B wins today — it's close enough to 405B on reasoning and coding that the 6–8x cost gap is almost impossible to justify. Llama 3.1 405B remains the right pick for research, as a fine-tune donor, or when you absolutely need the biggest open-weights dense model available under the Llama Community License.
When to choose each
Choose Llama 3.1 405B if…
- You're doing research on frontier open models.
- You want to distill into smaller task-specific models.
- You have 8x H100 capacity or more and compute isn't the blocker.
- You need the absolute top score on open benchmarks.
Choose Llama 3.3 70B if…
- You want near-frontier quality at realistic serving cost.
- You have 2x H100 / 4x A100 class capacity.
- You want the best open default for agents and RAG.
- You're evaluating self-hosted vs API and want parity with hosted GPT-4o-class tier.
Frequently asked questions
Is Llama 3.3 70B really as good as 405B?
On most public benchmarks, 3.3 70B is within a couple of points of 3.1 405B on reasoning, coding, and multilingual tests. The gap widens on very long-context and some niche benchmarks, but the cost-quality curve strongly favours 70B.
Can I run Llama 3.1 405B on the VSET IDEA Lab?
Not for real-time inference — 405B realistically needs 8x H100 or equivalent. For student work, 70B (or quantised 70B) on 2x H100 is a much better target.
Which is better for fine-tuning?
70B for practical fine-tuning on small clusters and almost all course projects. 405B fine-tuning is a research-scale effort that typically happens on rented compute.
Sources
- Meta — Llama 3.1 announcement — accessed 2026-04-20
- Meta — Llama 3.3 release — accessed 2026-04-20