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DeepSeek-V3 vs Mistral NeMo Instruct

DeepSeek-V3 leads the LLM Stats Score 16.0 to -4.5. Mistral NeMo Instruct is 3.2x cheaper per token.

DeepSeek · Mistral AI · Updated for 2026

Which is better?

DeepSeek-V3 leads the overall LLM Stats Score 16.0 to -4.5, ranking #208 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V3 wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Mistral NeMo Instruct is roughly 3.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V3 also accepts a larger context window (131,072 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-V3

  • overall performance matters — it scores 16.0 and ranks #208 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you process long inputs — it offers a 131,072 token context window
  • you want the most recent training data — it shipped Dec 2024

Choose Mistral NeMo Instruct

  • cost matters — it's about 3.2x cheaper per token

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Mistral NeMo Instruct
Mistral AI
Core performance indexes
LLM Stats Score
16.0
#208
-4.5
#334
Reasoning
15.0
#207
-4.8
#325
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.27 / M
$0.15 / M
Output price
$1.10 / M
$0.15 / M
Context window
131,072
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V3
Mistral NeMo Instruct
Finance
19.9#79
-7.7#205
Legal
19.9#64
-7.7#189
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 8 for Mistral NeMo Instruct

1 shared

DeepSeek-V3 outperforms in 1 benchmarks (MMLU), while Mistral NeMo Instruct is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Sun Aug 30 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Mistral NeMo Instruct costs less

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

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 7.3x more expensive than Mistral NeMo Instruct ($0.15/1M tokens).

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

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

Lowest available price from all providers
Sun Aug 30 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
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 has 659.0B more parameters than Mistral NeMo Instruct, making it 5491.7% larger.

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

Context Window

Maximum input and output token capacity

DeepSeek-V3 accepts 131,072 input tokens compared to Mistral NeMo Instruct's 128,000 tokens. DeepSeek-V3 can generate longer responses up to 131,072 tokens, while Mistral NeMo Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Mistral AI
Mistral NeMo Instruct
Input128,000 tokens
Output128,000 tokens
Sun Aug 30 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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

MIT + Model License (Commercial use allowed)

Open weights

Mistral NeMo Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Mistral NeMo Instruct was released on 2024-07-18.

DeepSeek-V3 is 5 months newer than Mistral NeMo Instruct.

DeepSeek-V3

Dec 25, 2024

1.7 years ago

5mo newer
Mistral NeMo Instruct

Jul 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.

No cutoff dates available

Provider Availability

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

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

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

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Mistral NeMo Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Mistral NeMo Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs Mistral NeMo Instruct.

Which is better, DeepSeek-V3 or Mistral NeMo Instruct?

DeepSeek-V3 leads the LLM Stats Score 16.0 to -4.5. DeepSeek-V3 is made by DeepSeek and Mistral NeMo Instruct is made by Mistral AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3 compare to Mistral NeMo Instruct in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Mistral NeMo Instruct scores HellaSwag: 83.5%, Winogrande: 76.8%, TriviaQA: 73.8%, CommonSenseQA: 70.4%, MMLU: 68.0%.

Is DeepSeek-V3 cheaper than Mistral NeMo Instruct?

Mistral NeMo Instruct is 1.8x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. Mistral NeMo Instruct costs $0.15/M input and $0.15/M output via google.

What are the context window sizes for DeepSeek-V3 and Mistral NeMo Instruct?

DeepSeek-V3 supports 131K tokens and Mistral NeMo Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3 and Mistral NeMo Instruct?

Key differences include LLM Stats Score (16.0 vs -4.5), context window (131K vs 128K), input pricing ($0.27 vs $0.15/M), licensing (MIT + Model License (Commercial use allowed) vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Mistral NeMo Instruct?

DeepSeek-V3 is developed by DeepSeek and Mistral NeMo Instruct is developed by Mistral AI.

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