Grok 4.20 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 Grok 4.20 and Mixtral 8x22B.
Grok 4.20
Grok 4.20 is a reasoning model from xAI with industry-leading speed and agentic tool calling capabilities. It combines the lowest hallucination rate on the market with strict prompt adherance, delivering...
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 | Grok 4.20 | Mixtral 8x22B |
|---|---|---|
| Provider | xAI | Mistral |
| Context Window | 2,000,000 tokens | 65,536 tokens |
| Agent Suitability | N/A | 87/100 |
| Time to First Token (TTFT) | N/A | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-03-31 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Grok 4.20
$1.25
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Grok 4.20
$2.50
Mixtral 8x22B
$1.00
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Grok 4.20 Quirks & Gotchas
No developer gotchas reported.
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