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GLM 5.2 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 GLM 5.2 and Mixtral 8x22B.

Zhipu AI

GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

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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

SpecificationGLM 5.2Mixtral 8x22B
ProviderZhipu AIMistral
Context Window1,048,576 tokens65,536 tokens
Agent SuitabilityN/A87/100
Time to First Token (TTFT)N/A320 ms
Deployment Modelmanaged apiself hostable
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-06-162024-12-11

API Pricing Comparison

Input Price per Million Tokens

GLM 5.2

$0.93

Mixtral 8x22B

$0.50

Output Price per Million Tokens

GLM 5.2

$3.00

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.

MMLUGeneral knowledge & multi-task understanding
8950.0%vsN/A
GLM 5.2
Mixtral 8x22B
HumanEvalPython coding & logic synthesis
9120.0%vsN/A
GLM 5.2
Mixtral 8x22B
MATHComplex mathematical problem solving
8050.0%vsN/A
GLM 5.2
Mixtral 8x22B
GPQAGraduate-level expert reasoning
5350.0%vsN/A
GLM 5.2
Mixtral 8x22B
HellaSwagCommonsense reasoning and inference
8980.0%vsN/A
GLM 5.2
Mixtral 8x22B
MT-BenchMulti-turn conversation flow quality
930.0%vsN/A
GLM 5.2
Mixtral 8x22B

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.

Mixtral 8x22B Quirks & Gotchas

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