Llama 3.3 70B Instruct vs Mixtral 8x22B
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 3.3 70B Instruct and Mixtral 8x22B.
Llama 3.3 70B Instruct
Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.
Mixtral 8x22B
Mixtral 8x22B is Mistral AI's open-weight Mixture-of-Experts model, activating only 39B of its 141B total parameters per token to deliver frontier-level performance at inference costs comparable to a much smaller dense model. Released under the Apache 2.0 license, Mixtral 8x22B is one of the most capable fully open-weight models available, with strong multilingual performance, robust coding ability, and efficient fine-tuning via LoRA. It is widely deployed across self-hosted infrastructure, including Ollama, vLLM, and Hugging Face TGI.
Technical Specifications
| Specification | Llama 3.3 70B Instruct | Mixtral 8x22B |
|---|---|---|
| Provider | Meta | Mistral |
| Context Window | 131,072 tokens | 65,536 tokens |
| Agent Suitability | 83/100 | 87/100 |
| Time to First Token (TTFT) | 280 ms | 320 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-12-06 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.3 70B Instruct
$0.10
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Llama 3.3 70B Instruct
$0.32
Mixtral 8x22B
$1.00
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Llama 3.3 70B Instruct Quirks & Gotchas
- ▸Stable, well-documented self-hosted option with strong community support
- ▸Outperformed by Llama 4 Maverick for agentic tool-calling workflows
Mixtral 8x22B Quirks & Gotchas
- ▸MoE architecture — efficient inference for its capability tier
- ▸Requires ~90GB VRAM at FP16 — 4-bit quantization recommended for single-GPU deployment