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Mixtral 8x22BvsMistral Small

Mistral AI vs Mistral AI — Side-by-side model comparison

Mixtral 8x22B leads 3/5 categories

Head-to-Head Comparison

MetricMixtral 8x22BMistral Small
Provider
Arena Rank
#16
#19
Context Window
64K
32K
Input Pricing
$0.90/1M tokens
$0.20/1M tokens
Output Pricing
$2.70/1M tokens
$0.60/1M tokens
Parameters
176B (39B active)
22B
Open Source
Yes
Yes
Best For
Efficient reasoning, multilingual, coding
Fast inference, cost-effective tasks, chat
Release Date
Apr 17, 2024
Sep 18, 2024

Mixtral 8x22B

Mixtral 8x22B, developed by Mistral AI, is a large Mixture-of-Experts model with 176 billion total parameters (39 billion active per token) and a 64K token context window. The model scales the MoE architecture to deliver stronger reasoning, coding, and multilingual performance while maintaining the efficiency advantages of sparse expert routing. It supports function calling and structured outputs for production agentic workflows. Free and open-source, Mixtral 8x22B can be deployed on enterprise GPU infrastructure for organizations requiring powerful, self-hosted AI. Priced at $0.90 per million input tokens through API providers. The model demonstrates competitive performance with proprietary models at significantly lower operational cost due to its efficient architecture. Mixtral 8x22B ranks #16 on the Chatbot Arena leaderboard, confirming strong capability for an open-weight MoE model.

View Mistral AI profile →

Mistral Small

Mistral Small, developed by Mistral AI, is a compact 22 billion parameter model with a 32K token context window optimized for fast inference and low deployment costs. The model handles coding, summarization, classification, and conversational tasks while maintaining the quality standards established by the Mistral model family. Its small footprint makes it suitable for edge deployment, cost-sensitive production applications, and use cases requiring low-latency responses. Priced at $0.20 per million input tokens and $0.60 per million output tokens, it offers affordable access to Mistral's technology. As an open-source model, it can also be self-hosted without API costs. Mistral Small ranks #19 on the Chatbot Arena leaderboard, demonstrating competitive performance for its compact size and establishing it as a strong option for budget-conscious deployments.

View Mistral AI profile →

Key Differences: Mixtral 8x22B vs Mistral Small

1

Mixtral 8x22B ranks higher in arena benchmarks (#16) indicating stronger overall performance.

2

Mistral Small is 4.5x cheaper on average, making it the better choice for high-volume applications.

3

Mixtral 8x22B supports a larger context window (64K), allowing it to process longer documents in a single request.

4

Mixtral 8x22B has 176B (39B active) parameters vs Mistral Small's 22B, which affects inference speed and capability.

M

When to use Mixtral 8x22B

  • +You need the highest quality output based on arena rankings
  • +Quality matters more than cost
  • +You need to process long documents (64K context)
  • +Your use case involves efficient reasoning, multilingual, coding
View full Mixtral 8x22B specs →
M

When to use Mistral Small

  • +Budget is a concern and you need cost efficiency
  • +Your use case involves fast inference, cost-effective tasks, chat
View full Mistral Small specs →

Cost Analysis

At current pricing, Mistral Small is 4.5x more affordable than Mixtral 8x22B. For a typical enterprise workload processing 100M tokens per month:

Mixtral 8x22B monthly cost

$180

100M tokens/mo (50/50 in/out)

Mistral Small monthly cost

$40

100M tokens/mo (50/50 in/out)

The Verdict

Mixtral 8x22B wins our head-to-head comparison with 3 out of 5 category wins. It's the stronger choice for efficient reasoning, multilingual, coding, though Mistral Small holds an edge in fast inference, cost-effective tasks, chat. If cost is your primary concern, Mistral Small offers better value.

Last compared: April 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Mixtral 8x22B or Mistral Small?
In our head-to-head comparison, Mixtral 8x22B leads in 3 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Mixtral 8x22B excels at efficient reasoning, multilingual, coding, while Mistral Small is better suited for fast inference, cost-effective tasks, chat. The best choice depends on your specific requirements, budget, and use case.
How does Mixtral 8x22B pricing compare to Mistral Small?
Mixtral 8x22B charges $0.90 per 1M input tokens and $2.70 per 1M output tokens. Mistral Small charges $0.20 per 1M input tokens and $0.60 per 1M output tokens. Mistral Small is the more affordable option, approximately 4.5x cheaper on average. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Mixtral 8x22B and Mistral Small?
Mixtral 8x22B supports a 64K token context window, while Mistral Small supports 32K tokens. Mixtral 8x22B can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Mixtral 8x22B or Mistral Small for free?
Mixtral 8x22B is a paid API model starting at $0.90 per 1M input tokens. Mistral Small is a paid API model starting at $0.20 per 1M input tokens. Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Mixtral 8x22B or Mistral Small?
Mixtral 8x22B holds arena rank #16, while Mistral Small holds rank #19. Mixtral 8x22B performs better in overall arena benchmarks, which aggregate human preference ratings across coding, reasoning, and general tasks. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Mixtral 8x22B or Mistral Small better for coding?
Mixtral 8x22B is specifically optimized for coding tasks. Mistral Small's primary strength is fast inference, cost-effective tasks, chat. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.