DeepSeek V4 Pro 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 DeepSeek V4 Pro and Mixtral 8x22B.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
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 | DeepSeek V4 Pro | Mixtral 8x22B |
|---|---|---|
| Provider | DeepSeek | Mistral |
| Context Window | 1,048,576 tokens | 65,536 tokens |
| Agent Suitability | 94/100 | 87/100 |
| Time to First Token (TTFT) | 280 ms | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Mixtral 8x22B
$0.50
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
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.
DeepSeek V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
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