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GPT-5.2 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-5.2 Pro and Mixtral 8x22B.

OpenAI

GPT-5.2 Pro

GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning,...

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Mistral

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.

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Technical Specifications

SpecificationGPT-5.2 ProMixtral 8x22B
ProviderOpenAIMistral
Context Window400,000 tokens65,536 tokens
Agent SuitabilityN/A87/100
Time to First Token (TTFT)N/A320 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-12-102024-12-11

API Pricing Comparison

Input Price per Million Tokens

GPT-5.2 Pro

$21.00

Mixtral 8x22B

$0.50

Output Price per Million Tokens

GPT-5.2 Pro

$168.00

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-5.2 Pro 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