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MiniMax M3 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 MiniMax M3 and Mixtral 8x22B.

MiniMax

MiniMax M3

MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...

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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

SpecificationMiniMax M3Mixtral 8x22B
ProviderMiniMaxMistral
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 Date2026-05-312024-12-11

API Pricing Comparison

Input Price per Million Tokens

MiniMax M3

$0.30

Mixtral 8x22B

$0.50

Output Price per Million Tokens

MiniMax M3

$1.20

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
8400.0%vsN/A
MiniMax M3
Mixtral 8x22B
HumanEvalPython coding & logic synthesis
8350.0%vsN/A
MiniMax M3
Mixtral 8x22B
MATHComplex mathematical problem solving
6200.0%vsN/A
MiniMax M3
Mixtral 8x22B
GPQAGraduate-level expert reasoning
4250.0%vsN/A
MiniMax M3
Mixtral 8x22B
HellaSwagCommonsense reasoning and inference
8520.0%vsN/A
MiniMax M3
Mixtral 8x22B
MT-BenchMulti-turn conversation flow quality
888.0%vsN/A
MiniMax M3
Mixtral 8x22B

MiniMax M3 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