GPT-5.5 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 GPT-5.5 Pro and Mixtral 8x22B.
GPT-5.5 Pro
GPT-5.5 Pro is OpenAI’s high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It features a 1M+ token context window (922K input, 128K output) with support for...
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 | GPT-5.5 Pro | Mixtral 8x22B |
|---|---|---|
| Provider | OpenAI | Mistral |
| Context Window | 1,050,000 tokens | 65,536 tokens |
| Agent Suitability | 98/100 | 87/100 |
| Time to First Token (TTFT) | 450 ms | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
GPT-5.5 Pro
$30.00
Mixtral 8x22B
$0.50
Output Price per Million Tokens
GPT-5.5 Pro
$180.00
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.
GPT-5.5 Pro Quirks & Gotchas
- ▸Best-in-class instruction following for complex agentic chains
- ▸Premium pricing — use GPT-5 for cost-sensitive workloads
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