DeepSeek-V4-Pro-0813 vs Mistral NeMo Instruct
Comparing DeepSeek-V4-Pro-0813 and Mistral NeMo Instruct across benchmarks, pricing, and capabilities.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Mistral NeMo Instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Mistral NeMo Instruct is roughly 3.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Mistral NeMo Instruct
- cost matters — it's about 3.6x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 and Mistral NeMo Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 2.9x more expensive than Mistral NeMo Instruct ($0.15/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 5.8x more expensive than Mistral NeMo Instruct ($0.15/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than Mistral NeMo Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1588.0B more parameters than Mistral NeMo Instruct, making it 13233.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Mistral NeMo Instruct's 128,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Mistral NeMo Instruct is limited to 128,000 tokens.
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while Mistral NeMo Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Mistral NeMo Instruct was released on 2024-07-18.
DeepSeek-V4-Pro-0813 is 25 months newer than Mistral NeMo Instruct.
Aug 13, 2026
1 weeks ago
2.1yr newerJul 18, 2024
2.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Mistral NeMo Instruct is available from Google, Mistral AI.
DeepSeek-V4-Pro-0813
Mistral NeMo Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Mistral NeMo Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Mistral NeMo Instruct.