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Mixtral 8x22B vs Voxtral Small 24B 2507

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 Mixtral 8x22B and Voxtral Small 24B 2507.

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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Mistral

Voxtral Small 24B 2507

Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...

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Technical Specifications

SpecificationMixtral 8x22BVoxtral Small 24B 2507
ProviderMistralMistral
Context Window65,536 tokens32,000 tokens
Agent Suitability87/100N/A
Time to First Token (TTFT)320 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-12-112025-10-30

API Pricing Comparison

Input Price per Million Tokens

Mixtral 8x22B

$0.50

Voxtral Small 24B 2507

$0.10

Output Price per Million Tokens

Mixtral 8x22B

$1.00

Voxtral Small 24B 2507

$0.30

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.

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

Voxtral Small 24B 2507 Quirks & Gotchas

No developer gotchas reported.