Model Comparison

DeepSeek-V3.1 vs Mistral NeMo Instruct

Comparing DeepSeek-V3.1 and Mistral NeMo Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.1 and Mistral NeMo Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Mistral NeMo Instruct costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 1.8x more expensive than Mistral NeMo Instruct ($0.15/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 6.7x more expensive than Mistral NeMo Instruct ($0.15/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than Mistral NeMo Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon Apr 20 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Mistral AI
Mistral NeMo Instruct
Input tokens$0.15
Output tokens$0.15
Best providerGoogle
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

659.0B diff

DeepSeek-V3.1 has 659.0B more parameters than Mistral NeMo Instruct, making it 5491.7% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Mistral AI
Mistral NeMo Instruct
12.0Bparameters
671.0B
DeepSeek-V3.1
12.0B
Mistral NeMo Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to Mistral NeMo Instruct's 128,000 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Mistral NeMo Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Mistral AI
Mistral NeMo Instruct
Input128,000 tokens
Output128,000 tokens
Mon Apr 20 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.1 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.

DeepSeek-V3.1

MIT

Open weights

Mistral NeMo Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Mistral NeMo Instruct was released on 2024-07-18.

DeepSeek-V3.1 is 6 months newer than Mistral NeMo Instruct.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

5mo newer
Mistral NeMo Instruct

Jul 18, 2024

1.8 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V3.1 is available from DeepInfra, Novita. Mistral NeMo Instruct is available from Google, Mistral AI.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

Mistral NeMo Instruct

google logo
Google
Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M
mistral logo
Mistral
Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Mistral AI
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Mistral AI
Mistral NeMo Instruct

FAQ

Common questions about DeepSeek-V3.1 vs Mistral NeMo Instruct

DeepSeek-V3.1 (DeepSeek) and Mistral NeMo Instruct (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Mistral NeMo Instruct scores HellaSwag: 83.5%, Winogrande: 76.8%, TriviaQA: 73.8%, CommonSenseQA: 70.4%, MMLU: 68.0%.
Mistral NeMo Instruct is 1.8x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Mistral NeMo Instruct costs $0.15/M input and $0.15/M output via google.
DeepSeek-V3.1 supports 164K tokens and Mistral NeMo Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (164K vs 128K), input pricing ($0.27 vs $0.15/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.1 is developed by DeepSeek and Mistral NeMo Instruct is developed by Mistral AI.