DeepSeek V4 Flash 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 Flash and Mixtral 8x22B.
DeepSeek V4 Flash
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
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 Flash | Mixtral 8x22B |
|---|---|---|
| Provider | DeepSeek | Mistral |
| Context Window | 1,048,576 tokens | 65,536 tokens |
| Agent Suitability | 86/100 | 87/100 |
| Time to First Token (TTFT) | 120 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 Flash
$0.09
Mixtral 8x22B
$0.50
Output Price per Million Tokens
DeepSeek V4 Flash
$0.18
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 Flash Quirks & Gotchas
- ▸Best cost-per-token ratio of any hosted API — ideal for high-throughput pipelines
- ▸Lower agentic performance vs V4 Pro — route complex tool calls accordingly
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