latentbrief

Model comparison

Mistral Large vs DeepSeek V4 Pro

The most significant observable difference between DeepSeek V4 Pro and Mistral Large is their token context capacity, with DeepSeek V4 Pro supporting up to 1,048,576 tokens versus Mistral Large's 128,000 tokens.

Specs

MetricMistral LargeDeepSeek V4 Pro
Context window128K tokens1.0M tokens
Input $/1M tokens$2.00$0.435
Output $/1M tokens$6.00$0.870
ModalitiesTextText
Open weightsNoYes
ReleasedDec 2024

Capability differences

CapabilityMistral LargeDeepSeek V4 Pro
Prompt cachingNoYes
Open weightsNoYes

How they differ

Context handling

Mistral Large

Mistral Large supports a 128,000 token context, suitable for medium-sized conversations and focused text analysis without exceeding its window size.

DeepSeek V4 Pro

DeepSeek V4 Pro supports an industry-leading 1,048,576 token context, enabling the ability to process vast text inputs and capture long-term dependencies.

Cost profile

Mistral Large

Mistral Large has higher per-token input ($2.0/1M) and output costs ($6.0/1M), which may cater to specialized use cases.

DeepSeek V4 Pro

DeepSeek V4 Pro offers lower per-token input ($0.435/1M) and output costs ($0.87/1M), making it economically viable for high-volume operations.

Speed

Mistral Large

Mistral Large is optimized for faster inference times on tasks within its relatively smaller context window.

DeepSeek V4 Pro

DeepSeek V4 Pro may be slower for large-scale tasks due to its extended context, which increases computational overhead.

Mistral Large — what sets it apart

  • +Mistral Large emphasizes faster processing and is optimized for tasks requiring accurate, targeted reasoning within constrained contexts.
  • +Its design focuses on efficiency for smaller-scale inputs.

DeepSeek V4 Pro — what sets it apart

  • +DeepSeek V4 Pro is designed for handling extremely large contexts and analyzing vast amounts of data in a single prompt.
  • +It employs a cost-efficient pricing model that benefits high-volume applications.

The most consequential difference lies in the contrast between DeepSeek V4 Pro's ability to handle massive context sizes and Mistral Large's speed and efficiency within a smaller token range.

Analysis synthesized from gpt-4o, llama-4-maverick, phi-4, etc.