Capability · Comparison
Mixtral 8x22B vs Llama 3.1 70B Instruct
A direct architectural comparison: Mistral's 8x22B MoE (39B active of 141B total) against Meta's fully dense Llama 3.1 70B. Both are Apache-permissive open-weight releases. Mixtral wins on inference cost per token; Llama wins on simplicity of deployment and English conversational quality.
Side-by-side
| Criterion | Mixtral 8x22B | Llama 3.1 70B Instruct |
|---|---|---|
| Architecture | MoE: 141B total, 39B active | Dense 70B |
| License | Apache 2.0 | Llama 3.1 Community License |
| Context window | 64,000 tokens | 128,000 tokens |
| MMLU | ~77% | ~83% |
| Coding (HumanEval) | ~76% | ~80% |
| Inference cost per token | Low — 39B active | Moderate — 70B active |
| VRAM (bf16) | ~280GB weights, 4xH100 minimum | ~140GB weights, 2xH100 minimum |
| Multilingual | Strong European languages | Strong broadly |
| Status in 2026 | Legacy, still used for cost-sensitive inference | Superseded by Llama 3.3 70B and Llama 4 |
Verdict
Mixtral 8x22B's MoE architecture was a compelling serving-cost story in 2024 — activating only 39B parameters per token for near-70B-dense quality. Llama 3.1 70B won on raw quality and ecosystem. In 2026 both are legacy; teams running cost-sensitive open-weight inference tend to have moved to Qwen 2.5 or Llama 3.3 successors. If you're already on one of these, stay; for new work, upgrade.
When to choose each
Choose Mixtral 8x22B if…
- Inference throughput per GPU-hour is the primary cost driver.
- You have 4+ H100-class GPUs and can exploit MoE routing.
- You need Apache 2.0 licensing with no community-license friction.
- Your stack is already optimized for Mixtral-family routing kernels.
Choose Llama 3.1 70B Instruct if…
- You want the simpler dense-transformer serving story.
- You only have 2xH100 and need to fit weights on that footprint.
- English conversational quality and broader ecosystem matter.
- You need 128k context in a single call.
Frequently asked questions
Is MoE always cheaper than dense?
Per token generated, usually yes — you activate fewer parameters. But total VRAM is higher because you must keep all experts in memory, so the break-even depends on your batch size and GPU count.
Should I still deploy Mixtral 8x22B in 2026?
Only if you already run it. Newer MoE models (DeepSeek V3, Llama 4 Maverick) have better quality/cost and similar architecture.
What's the biggest quality gap?
Llama 3.1 70B is notably better at English conversational quality and reasoning benchmarks. Mixtral is competitive on European languages and much cheaper per token to serve.
Sources
- Mistral AI — Mixtral 8x22B — accessed 2026-04-20
- Meta — Llama 3.1 — accessed 2026-04-20