DeepSeek V4 Pro vs DeepSeek V3.2

Tested Gemma 4 (31B) on our benchmark. Genuinely did not expect this.

100% survival, 5 out of 5 runs profitable, +1,144% median ROI. At $0.20 per run.

It outperforms GPT-5.2 ($4.43/run), Gemini 3 Pro ($2.95/run), Sonnet 4.6 ($7.90/run), and absolutely destroys every Chinese open-source model we've tested — Qwen 3.5 397B, Qwen 3.5 9B, DeepSeek V3.2, GLM-5. None of them even survive consistently.

The only model that beats Gemma 4 is Opus 4.6 at $36 per run. That's 180× more expensive.

31 billion parameters. Twenty cents. We double-checked the config, the prompt, the model ID — everything is identical to every other model on the leaderboard. Same seed, same tools, same simulation. It's just this good.

Strongly recommend trying it for your agentic workflows. We've tested 22 models so far and this is by far the best cost-to-performance ratio we've ever seen.

Full breakdown with charts and day-by-day analysis: [foodtruckbench.com/blog/gemma-4-31b](https://foodtruckbench.com/blog/gemma-4-31b)

*FoodTruck Bench is an AI business simulation benchmark — the agent runs a food truck for 30 days, making decisions about location, menu, pricing, staff, and inventory. Leaderboard at* [*foodtruckbench.com*](https://foodtruckbench.com)

**EDIT — Gemma 4 26B A4B results are in.**

Lots of you asked about the 26B A4B variant. Ran 5 simulations, here's the honest picture:

**60% survival** (3/5 completed, 2 bankrupt). Median ROI: +119%, Net Worth: $4,386. Cost: $0.31/run. Placed #7 on the leaderboard — above every Chinese model and Sonnet 4.5, below everything else.

Both bankruptcies were loan defaults — same pattern we see across models. The 3 surviving runs were solid, especially the best one at +296% ROI.

**But here's the catch.** The 26B A4B is the only model out of 23 tested that required custom output sanitization to function. It produces valid tool-call intent, but the JSON formatting is consistently broken — malformed quotes, trailing garbage tokens, invalid escapes. I had to build a 3-stage sanitizer specifically for this model. No other model needed anything like this. The business decisions themselves are unmodified — the sanitizer only fixes JSON formatting, not strategy. But if you're planning to use this model in agentic workflows, be prepared to handle its output format. It does not produce clean function calls out of the box.

**TL;DR:** 31B dense → 100% survival, $0.20/run, #3 overall. 26B A4B → 60% survival, $0.31/run, #7 overall, but requires custom output parsing. The 31B is the clear winner. Updated leaderboard: foodtruckbench.com

# Introduction

We introduce **DeepSeek-V3.2**, a model that harmonizes high computational efficiency with superior reasoning and agent performance. Our approach is built upon three key technical breakthroughs:

1. **DeepSeek Sparse Attention (DSA):** We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios.
2. **Scalable Reinforcement Learning Framework:** By implementing a robust RL protocol and scaling post-training compute, *DeepSeek-V3.2* performs comparably to GPT-5. Notably, our high-compute variant, **DeepSeek-V3.2-Speciale**, **surpasses GPT-5** and exhibits reasoning proficiency on par with Gemini-3.0-Pro.
* *Achievement:* 🥇 **Gold-medal performance** in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI).
3. **Large-Scale Agentic Task Synthesis Pipeline:** To integrate **reasoning into tool-use** scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This facilitates scalable agentic post-training, improving compliance and generalization in complex interactive environments.

[https://huggingface.co/collections/deepseek-ai/deepseek-v32-68da2f317324c70047c28f66](https://huggingface.co/collections/deepseek-ai/deepseek-v32-68da2f317324c70047c28f66)

https://api-docs.deepseek.com/news/news250929

TLDR: It's a near linear model with almost O(kL) attention complexity.

Paper link: [https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/DeepSeek\_V3\_2.pdf](https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/DeepSeek_V3_2.pdf)

According to their paper, the Deepseek Sparse Attention computes attention for only k selected previous tokens, meaning it's a linear attention model with decoding complexity O(kL). What's different from previous linear models is it has a O(L\^2) index selector to select the tokens to compute attention for. Even though the index selector has square complexity but it's fast enough to be neglected.

https://preview.redd.it/h0zys7b4o3sf1.png?width=1390&format=png&auto=webp&s=00a7ea8ada91109d417b8d6e3f490ae9743c18b2

https://preview.redd.it/has2qyz7o3sf1.png?width=1300&format=png&auto=webp&s=0742135b2cb1be9bd853b614097597d521a4ef54

[Cost for V3.2 only increase very little thanks to linear attention](https://preview.redd.it/053i7pdro3sf1.png?width=1356&format=png&auto=webp&s=52adfb1bf9d0ee03f0a7d8e7b31340ab63b2f4b4)

Previous linear model attempts for linear models from other teams like Google and Minimax have not been successful. Let's see if DS can make the breakthrough this time.