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GPT-5.1 Chat 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-5.1 Chat and Mixtral 8x22B.

OpenAI

GPT-5.1 Chat

GPT-5.1 Chat (AKA Instant is the fast, lightweight member of the 5.1 family, optimized for low-latency chat while retaining strong general intelligence. It uses adaptive reasoning to selectively β€œthink” on...

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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-5.1 ChatMixtral 8x22B
ProviderOpenAIMistral
Context Window128,000 tokens65,536 tokens
Agent SuitabilityN/A87/100
Time to First Token (TTFT)N/A320 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-11-132024-12-11

API Pricing Comparison

Input Price per Million Tokens

GPT-5.1 Chat

$1.25

Mixtral 8x22B

$0.50

Output Price per Million Tokens

GPT-5.1 Chat

$10.00

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-5.1 Chat Quirks & Gotchas

No developer gotchas reported.

Mixtral 8x22B Quirks & Gotchas

  • β–ΈMoE architecture β€” efficient inference for its capability tier
  • β–ΈRequires ~90GB VRAM at FP16 β€” 4-bit quantization recommended for single-GPU deployment