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Mistral vs Perplexity: Token Pricing, Speed & Intelligence

Full comparison of Mistral and Perplexity — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

Mistral

European frontier AI — Mistral Large, Codestral, and open models

Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting.

CodingEuropean complianceOpen-sourceCost-efficiencyChat
Proprietary modelsHosts open weights

Perplexity

Sonar — search-augmented LLMs with real-time web grounding

Perplexity's Sonar models are designed for search-augmented generation, combining LLM reasoning with real-time web retrieval. Unlike standard LLMs, Sonar responses include citations and are grounded in current web content. Ideal for research assistants, news summarisation, and fact-checking applications.

ResearchReal-time dataFact-checkingNews summarisationKnowledge bases
Proprietary models

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

Live token pricing

Strengths & weaknesses

Mistral

Several open-weight models available for self-hosting
Codestral purpose-built for code with 262K context
European data sovereignty — GDPR-native
Competitive pricing vs. GPT-4 class models
Mistral Small is one of the cheapest capable models at $0.10/1M
Intelligence scores trail OpenAI and Anthropic at frontier tier
Smaller ecosystem than OpenAI
No vision support on smaller models

Perplexity

Real-time web search with automatic citations
Sonar Pro for deep research with multi-step retrieval
Grounded responses reduce hallucination on factual queries
Competitive pricing for search-augmented generation
Simple API with OpenAI-compatible interface
Not suitable for tasks that don't benefit from web search
Less capable than frontier models on pure reasoning tasks
No vision or multimodal support

Key differentiators

Mistral

The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API.

Perplexity

Every Sonar response includes real-time web citations — the only LLM API purpose-built for grounded, verifiable answers.

Frequently asked questions

Mistral FAQs

How much does the Mistral API cost?

Mistral Large costs $2.00/1M input and $6.00/1M output tokens. Mistral Small is $0.10/$0.30 per 1M tokens — one of the cheapest capable models available. Codestral for code generation is priced separately.

Are Mistral models open-weight?

Some are. Mistral 7B, Mixtral 8x7B, and Mixtral 8x22B are open-weight and available on Hugging Face for self-hosting. Mistral Large and Codestral are proprietary and only available via the API.

What is Codestral?

Codestral is Mistral's code-specialised model with a 262K context window. It supports 80+ programming languages and is optimised for code completion, generation, and explanation tasks.

Perplexity FAQs

What is Perplexity Sonar?

Sonar is Perplexity's family of search-augmented LLMs. Unlike standard LLMs, Sonar automatically searches the web and includes citations in every response. Sonar is available in standard and Pro (deep research) variants.

How much does Perplexity API cost?

Perplexity charges per 1M tokens plus a per-request fee for search operations. Check their pricing page for current rates as they vary by model and search depth.

When should I use Perplexity instead of GPT-4o?

Use Perplexity when your application needs real-time, verifiable information with citations — research tools, news summarisation, fact-checking, or any use case where accuracy on current events matters. For creative tasks, coding, or reasoning without web grounding, GPT-4o or Claude are better choices.

Provider resources

MistralEuropean frontier AI — Mistral Large, Codestral, and open models

Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting.

The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API.

PerplexitySonar — search-augmented LLMs with real-time web grounding

Perplexity's Sonar models are designed for search-augmented generation, combining LLM reasoning with real-time web retrieval. Unlike standard LLMs, Sonar responses include citations and are grounded in current web content. Ideal for research assistants, news summarisation, and fact-checking applications.

Every Sonar response includes real-time web citations — the only LLM API purpose-built for grounded, verifiable answers.

Key strengths compared

Mistral

  • Several open-weight models available for self-hosting
  • Codestral purpose-built for code with 262K context
  • European data sovereignty — GDPR-native

Perplexity

  • Real-time web search with automatic citations
  • Sonar Pro for deep research with multi-step retrieval
  • Grounded responses reduce hallucination on factual queries

Provider category context

Mistral is a frontier lab, founded in 2023. Perplexity is a inference api, founded in 2022. Mistral as a frontier lab trains and serves its own proprietary models. Perplexity as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.

How to choose between them

Choose Mistral if you need several open-weight models available for self-hosting. Choose Perplexity if you need real-time web search with automatic citations. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.