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Mistral Large 2vsMistral Small

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

Mistral Large 2 leads 3/5 categories

Head-to-Head Comparison

MetricMistral Large 2Mistral Small
Provider
Arena Rank
#8
#19
Context Window
128K
32K
Input Pricing
$2.00/1M tokens
$0.20/1M tokens
Output Pricing
$6.00/1M tokens
$0.60/1M tokens
Parameters
123B
22B
Open Source
Yes
Yes
Best For
Multilingual, coding, complex reasoning
Fast inference, cost-effective tasks, chat
Release Date
Jul 24, 2024
Sep 18, 2024

Mistral Large 2

Mistral Large 2, developed by Mistral AI, is the company's most capable model with 123 billion parameters and a 128K token context window. The model excels at complex reasoning, coding, and multilingual tasks with particular strength across European languages. Mistral Large 2 supports function calling, JSON output, and system prompts for production deployments. As an open-source model, it can be deployed on enterprise infrastructure or accessed through Mistral's API, Azure, AWS, and Google Cloud. Priced at $2.00 per million input tokens and $6.00 per million output tokens through the API. It competes directly with GPT-4o and Claude Sonnet on quality benchmarks while offering deployment flexibility that proprietary models lack. Mistral Large 2 ranks #8 on the Chatbot Arena leaderboard, confirming its position as one of the strongest European-built AI models.

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: Mistral Large 2 vs Mistral Small

1

Mistral Large 2 ranks higher in arena benchmarks (#8) indicating stronger overall performance.

2

Mistral Small is 10.0x cheaper on average, making it the better choice for high-volume applications.

3

Mistral Large 2 supports a larger context window (128K), allowing it to process longer documents in a single request.

4

Mistral Large 2 has 123B parameters vs Mistral Small's 22B, which affects inference speed and capability.

M

When to use Mistral Large 2

  • +You need the highest quality output based on arena rankings
  • +Quality matters more than cost
  • +You need to process long documents (128K context)
  • +Your use case involves multilingual, coding, complex reasoning
View full Mistral Large 2 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 10.0x more affordable than Mistral Large 2. For a typical enterprise workload processing 100M tokens per month:

Mistral Large 2 monthly cost

$400

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

Mistral Small monthly cost

$40

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

The Verdict

Mistral Large 2 wins our head-to-head comparison with 3 out of 5 category wins. It's the stronger choice for multilingual, coding, complex reasoning, 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, Mistral Large 2 or Mistral Small?
In our head-to-head comparison, Mistral Large 2 leads in 3 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Mistral Large 2 excels at multilingual, coding, complex reasoning, 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 Mistral Large 2 pricing compare to Mistral Small?
Mistral Large 2 charges $2.00 per 1M input tokens and $6.00 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 10.0x 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 Mistral Large 2 and Mistral Small?
Mistral Large 2 supports a 128K token context window, while Mistral Small supports 32K tokens. Mistral Large 2 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 Mistral Large 2 or Mistral Small for free?
Mistral Large 2 is a paid API model starting at $2.00 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, Mistral Large 2 or Mistral Small?
Mistral Large 2 holds arena rank #8, while Mistral Small holds rank #19. Mistral Large 2 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 Mistral Large 2 or Mistral Small better for coding?
Mistral Large 2 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.