The AI arena is free today

Open Superagent

DeepSeek-V3.2 (Thinking) vs Mistral Large 4

Mistral Large 4 leads the LLM Stats Score 46.2 to 32.5. DeepSeek-V3.2 (Thinking) is 3.3x cheaper per token.

DeepSeek · Mistral AI · Updated for 2026

Which is better?

Mistral Large 4 leads the overall LLM Stats Score 46.2 to 32.5, ranking #34 overall.

On price, DeepSeek-V3.2 (Thinking) is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Mistral Large 4 also accepts a larger context window (1,000,000 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-V3.2 (Thinking)

  • cost matters — it's about 3.3x cheaper per token
  • you need open weights you can self-host or fine-tune

Choose Mistral Large 4

  • overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you process long inputs — it offers a 1,000,000 token context window
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

DeepSeek-V3.2 (Thinking)
DeepSeek
Mistral Large 4
Mistral AI
Core performance indexes
LLM Stats Score
32.5
#118
46.2
#34
Reasoning
32.5
#114
44.4
#38
Coding
22.6
#92
35.4
#27
Agents
11.1
#124
34.0
#26
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.28 / M
$0.68 / M
Output price
$0.42 / M
$2.09 / M
Context window
131,072
1,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V3.2 (Thinking)
Mistral Large 4
Tool use
9.8#139
29.1#17
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for DeepSeek-V3.2 (Thinking) · 18 for Mistral Large 4

No common benchmarks found

DeepSeek-V3.2 (Thinking) and Mistral Large 4don'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

DeepSeek-V3.2 (Thinking) costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 2.4x cheaper than Mistral Large 4 ($0.68/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 5.0x cheaper than Mistral Large 4 ($2.09/1M tokens).

In conclusion, Mistral Large 4 is more expensive than DeepSeek-V3.2 (Thinking).*

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

Lowest available price from all providers
Fri Oct 16 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Mistral AI
Mistral Large 4
Input tokens$0.68
Output tokens$2.09
Best providerMistral
Notice missing or incorrect data?

Model Size

Parameter count comparison

365.0B diff

Mistral Large 4 has 365.0B more parameters than DeepSeek-V3.2 (Thinking), making it 53.3% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Mistral AI
Mistral Large 4
1.1Tparameters
685.0B
DeepSeek-V3.2 (Thinking)
1050.0B
Mistral Large 4

Context Window

Maximum input and output token capacity

Mistral Large 4 accepts 1,000,000 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Mistral AI
Mistral Large 4
Input1,000,000 tokens
Output- tokens
Fri Oct 16 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Mistral Large 4 supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

Mistral Large 4

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Mistral Large 4 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Mistral Large 4

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Mistral Large 4 was released on 2026-10-06.

Mistral Large 4 is 10 months newer than DeepSeek-V3.2 (Thinking).

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

10 months ago

Mistral Large 4

Oct 6, 2026

5 days ago

10mo newer

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-V3.2 (Thinking) is available from DeepSeek. Mistral Large 4 is available from Mistral AI.

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Mistral Large 4

mistral logo
Mistral
Input Price:Input: $0.68/1MOutput Price:Output: $2.09/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Thinking) and Mistral Large 4 side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Thinking)
✓ Preferred
Mistral Large 4
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Mistral Large 4.

Which is better, DeepSeek-V3.2 (Thinking) or Mistral Large 4?

Mistral Large 4 leads the LLM Stats Score 46.2 to 32.5. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Mistral Large 4 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-V3.2 (Thinking) compare to Mistral Large 4 in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Mistral Large 4 scores B3 AI Security Benchmark: 93.3%, CyBench: 93.0%, SciCode: 91.8%, KORABench: 84.5%, CyberGym: 82.0%.

Is DeepSeek-V3.2 (Thinking) cheaper than Mistral Large 4?

DeepSeek-V3.2 (Thinking) is 2.4x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Mistral Large 4 costs $0.68/M input and $2.09/M output via mistral.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Mistral Large 4?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Mistral Large 4 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Thinking) and Mistral Large 4?

Key differences include LLM Stats Score (32.5 vs 46.2), context window (131K vs 1.0M), input pricing ($0.28 vs $0.68/M), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Mistral Large 4?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Mistral Large 4 is developed by Mistral AI.

Related comparisons