DeepSeek-V4.1-Flash vs Mistral NeMo Instruct
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to -4.8. Mistral NeMo Instruct is 15.2x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to -4.8, ranking #12 overall.
On price, Mistral NeMo Instruct is roughly 15.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Mistral NeMo Instruct
- cost matters — it's about 15.2x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 8 for Mistral NeMo Instruct
DeepSeek-V4.1-Flash and Mistral NeMo Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 11.6x more expensive than Mistral NeMo Instruct ($0.02/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 22.0x more expensive than Mistral NeMo Instruct ($0.03/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Mistral NeMo Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 751.2B more parameters than Mistral NeMo Instruct, making it 6260.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Mistral NeMo Instruct's 131,072 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Mistral NeMo Instruct is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Mistral NeMo Instruct does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Mistral NeMo Instruct
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while Mistral NeMo Instruct was released on 2024-07-18.
DeepSeek-V4.1-Flash is 26 months newer than Mistral NeMo Instruct.
Sep 10, 2026
2 days ago
2.1yr newerJul 18, 2024
2.2 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-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Mistral NeMo Instruct is available from DeepInfra, Google, Mistral AI.
DeepSeek-V4.1-Flash
Mistral NeMo Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Mistral NeMo Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Mistral NeMo Instruct.