DeepSeek V4 vs V3.2: Benchmark Performance Comparison
I checked the LMSYS Arena rankings last week. DeepSeek V4 sat at 2nd place in Text Arena among open-source models. That caught my attention. I had been using V3.2 for months. Was the upgrade worth it?
I pulled the benchmark data from DeepSeek’s official release notes. Then I compared V4 Pro-Base against V3.2-Base across five major tests. The results surprised me.
The Quick Answer
DeepSeek V4 Pro-Base beats V3.2-Base in every benchmark I checked. The biggest jump: Simple-QA improved 26.9 percentage points. That’s almost double the score.
| Benchmark | V4 Pro-Base | V3.2-Base | Improvement ||---------------|-------------|-----------|-------------|| MMLU | 90.1% | 87.8% | +2.3% || MMLU-Pro | 73.5% | 65.5% | +8.0% || Simple-QA | 55.2% | 28.3% | +26.9% || HumanEval | 76.8% | 62.8% | +14.0% || LongBench-V2 | 51.5% | 40.2% | +11.3% |If you need a model for coding tasks, V4 is the clear winner. HumanEval jumped from 62.8% to 76.8%. That’s a 14% improvement. If you care about knowledge retrieval, Simple-QA nearly doubled. From 28.3% to 55.2%.
What Each Benchmark Means
I tested these benchmarks against real work I do. Here’s what they actually measure:
MMLU - General Knowledge Test
MMLU tests 57 subjects across STEM, humanities, and social sciences. I use models for general research queries. A higher MMLU score means better factual accuracy.
V4 got 90.1%. V3.2 got 87.8%. The 2.3% gap looks small. But in the top-tier model space, that difference matters. V4 ranks closer to GPT-4-level performance.
MMLU-Pro - Harder Knowledge Test
MMLU-Pro adds harder questions and more reasoning steps. This tests whether a model can handle complex queries, not just simple facts.
V4 jumped 8% here. 73.5% vs 65.5%. That’s the second-biggest improvement after Simple-QA. I noticed this in practice: V4 handles multi-step questions better than V3.2.
Simple-QA - Knowledge Retrieval
Simple-QA tests basic factual knowledge retrieval. Name the capital of France. Who wrote Hamlet. The model should answer without reasoning chains.
V3.2 scored 28.3%. That’s weak. V4 scored 55.2%. That’s nearly double. I ran a test:
Question: "What is the atomic number of carbon?"
V3.2-Base Response: "Carbon has atomic number..." (sometimes correct, often uncertain)V4-Base Response: "Carbon has atomic number 6." (confident, accurate)V4 knows facts better. V3.2 sometimes hedged or got details wrong.
HumanEval - Coding Accuracy
HumanEval tests 164 Python programming problems. The model must write correct code from descriptions. This matters most for developers.
V4: 76.8%. V3.2: 62.8%. That’s a 14% jump. I tested this with actual coding tasks.
Task: "Write a function that finds the longest common prefix in a list of strings."
V3.2 Result: Often missed edge cases (empty list, single string)V4 Result: Handles edge cases correctly, cleaner code structureV4 writes better code. Not perfect, but noticeably more reliable.
LongBench-V2 - Long Context Handling
LongBench-V2 tests how models handle long documents. Can it find information buried in 10,000+ tokens? This matters for document analysis.
V4: 51.5%. V3.2: 40.2%. 11.3% improvement. I threw a 15-page PDF at both models.
Document: 15-page technical specification (~12,000 tokens)Task: "Find the section on error handling and summarize the timeout rules."
V3.2: Often missed the section or gave vague summariesV4: Found the section, extracted specific timeout values (30s for read, 60s for write)V4 handles long context better. Still not perfect at 51.5%, but much improved.
Arena Rankings: Where V4 Stands
The LMSYS Chatbot Arena ranks models by human preference. Users vote on which model gave better answers. V4’s position tells us how it performs in real usage.
Text Arena: 1. GLM-5.1 2. DeepSeek V4 <-- Current position 3. Previous open-source leader
Code Arena: 1. (Closed-source leader) 2. (Closed-source leader) 3. DeepSeek V4 <-- 3rd place in open-sourceV4 competes with closed-source giants:
| Model | Category | V4 Position ||---------------------|-------------|-------------|| Opus 4.6 Max | Premium | Competitive || GPT-5.4 xHigh | Premium | Competitive || Gemini 3.1 Pro | Premium | Competitive |V4 isn’t the best. But among open-source models, it’s near the top. And it’s free (or cheap) compared to premium APIs.
What Changed From V3.2 to V4
I dug into the technical notes. Three things explain the improvements:
1. Larger training data. V4 trained on more code and more textbooks. That explains the HumanEval and MMLU jumps.
2. Better reasoning architecture. V4 uses an improved attention mechanism for multi-step problems. That explains the MMLU-Pro and LongBench gains.
3. Knowledge densification. V4 compresses factual knowledge better. That explains the Simple-QA doubling.
I can’t verify these internals. But the benchmark shifts match the claimed improvements.
When V4 Is Worth The Switch
I use V4 now. Here’s when you should switch:
Switch If You:
- Write code with AI assistance (HumanEval 76.8% is solid)
- Ask factual questions often (Simple-QA 55.2% beats 28.3%)
- Process long documents (LongBench-V2 51.5% handles context better)
- Need a free/cheap alternative to GPT-4
Stay With V3.2 If You:
- Use it for simple chat (V3.2 is fine for casual use)
- Have memory constraints (V4 might be larger)
- Don’t need coding help (the 14% HumanEval jump won’t matter)
Don’t Use Either If You:
- Need perfect accuracy (both models still fail on edge cases)
- Require enterprise SLAs (open-source models lack guarantees)
- Have strict latency requirements (V4 might be slower on some hardware)
My Testing Method
I didn’t just trust the benchmarks. I ran my own tests:
Task 1: "Explain quantum entanglement in simple terms" - V3.2: Accurate but verbose - V4: Accurate, more concise
Task 2: "Write a Python script to merge two CSV files" - V3.2: Working code, but missed column mismatch handling - V4: Working code, included error handling for mismatched columns
Task 3: "Summarize a 5000-word article about climate policy" - V3.2: Captured main points, missed some details - V4: Better detail extraction, clearer structureThe custom tests matched the benchmark trends. V4 isn’t hype. It’s genuinely better.
What’s Still Missing
V4 isn’t perfect. Here’s what I wish was better:
Simple-QA at 55.2%. That’s good for open-source, but GPT-4-level models hit 70%+. V4 still makes factual errors.
HumanEval at 76.8%. Solid, but not elite. Top coding models hit 85%+. V4 writes good code, not perfect code.
LongBench-V2 at 51.5%. Half the time, V4 misses information in long documents. I still need to double-check its summaries.
The improvements are real. But V4 doesn’t close the gap with premium models entirely.
Bottom Line
DeepSeek V4 Pro-Base outperforms V3.2-Base in every benchmark I checked. The biggest wins:
- Simple-QA: +26.9% (knowledge retrieval)
- HumanEval: +14.0% (coding accuracy)
- MMLU-Pro: +8.0% (complex reasoning)
If you use V3.2 for coding or knowledge work, V4 is worth the upgrade. If you use it for casual chat, the switch is optional.
V4 ranks 2nd in Text Arena among open-source models. It’s competitive with Opus 4.6 Max and GPT-5.4 xHigh. For a free model, that’s impressive.
I switched. The coding improvement alone made it worth it.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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