DeepSeek V4 vs DeepSeek V3: How Much Has It Improved?
DeepSeek V3 — specifically V3.2 — was widely regarded as one of the best open-source models of 2025. So when DeepSeek V4 arrived in April 2026, the natural question was: how big is the leap? The answer, it turns out, is substantial — particularly in efficiency, context handling, and raw coding capability.
The Models Compared
| Feature | DeepSeek-V3.2 | DeepSeek-V4-Flash | DeepSeek-V4-Pro |
|---|---|---|---|
| Total Parameters | 671B | 284B | 1.6T |
| Active Parameters | 37B | 13B | 49B |
| Context Window | 128K tokens | 1M tokens | 1M tokens |
| Architecture | MoE + MLA | MoE + Hybrid Attention (CSA+HCA) + mHC | MoE + Hybrid Attention (CSA+HCA) + mHC |
| License | MIT | MIT | MIT |
| Reasoning Modes | Think / Non-think | Non-think / Think High / Think Max | Non-think / Think High / Think Max |
The most striking differences are:
- Context window: V3.2 offered 128K tokens; V4 offers 1 million — an 8× increase
- V4-Pro is 2.4× larger than V3.2 in total parameters
- Architecture: V4 introduces the Hybrid Attention system (CSA + HCA) and mHC, fundamentally changing long-context efficiency
- Reasoning modes: V3.2 had two modes; V4 introduces three with a more granular thinking budget control
Efficiency Gains: The Real Story
Perhaps the most impressive improvement isn't raw capability — it's efficiency at scale.
In a 1M-token context scenario, V4-Pro requires:
- Only 27% of the inference FLOPs that V3.2 would require at equivalent context lengths
- Only 10% of the KV cache memory that V3.2 would need
This is the core innovation of DeepSeek V4's Hybrid Attention Architecture (CSA + HCA). It's not just that V4 can handle 1M tokens — it's that it does so dramatically more efficiently than V3.2 could have at even 128K tokens.
Base Model Benchmark Comparison
| Benchmark | V3.2-Base | V4-Flash-Base | V4-Pro-Base |
|---|---|---|---|
| MMLU (5-shot) | 87.8% | 88.7% | 90.1% |
| MMLU-Redux (5-shot) | 87.5% | 89.4% | 90.8% |
| MMLU-Pro (5-shot) | 65.5% | 68.3% | 73.5% |
| HumanEval (Pass@1) | 62.8% | 69.5% | 76.8% |
| GSM8K (8-shot) | 91.1% | 90.8% | 92.6% |
| MATH (4-shot) | 60.5% | 57.4% | 64.5% |
| Simple-QA verified | 28.3% | 30.1% | 55.2% |
| LongBench-V2 | 40.2% | 44.7% | 51.5% |
| AGIEval | 80.1% | 82.6% | 83.1% |
Key takeaways:
- V4-Pro-Base improves over V3.2-Base across virtually every benchmark
- The most dramatic gains are in world knowledge (SimpleQA: 28.3% → 55.2%) and long-context (LongBench-V2: 40.2% → 51.5%)
- V4-Flash-Base, despite being smaller than V3.2, performs comparably or better on most tasks — a remarkable efficiency improvement
Coding: A Massive Leap
The coding improvement from V3.2 to V4-Pro is particularly dramatic, especially in Think Max mode:
| Benchmark | V3.2 (estimated) | V4-Pro Max |
|---|---|---|
| LiveCodeBench | ~75–80% | 93.5% |
| HumanEval (Base) | 62.8% | 76.8% |
| SWE-bench Verified | ~75% | 80.6% |
| Codeforces Rating | ~2500–2700 | 3206 |
The Codeforces rating jump from V3.2 to V4-Pro-Max represents a qualitative shift — V4-Pro is now among the elite tier of competitive programmers, a level V3.2 couldn't reach.
Context Window: From 128K to 1M
This deserves its own emphasis. DeepSeek V3.2's 128K context window was already generous — but it meant that large codebases, long legal documents, or multi-book research contexts needed chunking and summarization strategies.
V4's 1M-token context eliminates those workarounds entirely. The entire workflow changes:
V3.2 workflow for large documents:
- Split document into 120K-token chunks
- Summarize each chunk
- Combine summaries and reason over them
- Lose precision and context coherence
V4 workflow:
- Load the entire document in one context
- Ask your question directly
- Get a coherent, complete answer
New Training Innovations
V4 introduced significant training improvements over V3.2:
| Innovation | V3.2 | V4 |
|---|---|---|
| Optimizer | AdamW variant | Muon |
| Residual connections | Standard | mHC (Manifold-Constrained Hyper-Connections) |
| Training tokens | ~18T | 32T+ |
| Post-training pipeline | SFT + RL | Two-stage: expert specialization → on-policy distillation |
| Attention mechanism | MLA (Multi-head Latent Attention) | Hybrid Attention (CSA + HCA) |
These changes compound: more data, a better optimizer, stronger residual connections, and a revolutionary attention mechanism combine to produce the benchmark improvements we see in the results.
When Might You Still Use V3.2?
Despite V4's improvements, there are scenarios where V3.2 might still be preferred:
- Established fine-tunes: If you've already fine-tuned V3.2 for a specific task, retraining on V4 is significant work
- Smaller hardware: V3.2 at 671B total / 37B active still runs well on systems that might not handle V4-Flash (284B total)
- Stability: V4 is a preview release; V3.2 is a stable, battle-tested model
Conclusion
The jump from DeepSeek V3.2 to V4 is one of the largest capability leaps in a single model generation in recent AI history. The 8× context window expansion, fundamental architectural changes, and benchmark improvements across every category make V4 a clear upgrade for most use cases.
For developers and teams using V3.2 today — whether directly or through platforms like Framia.pro — migrating to V4-Flash or V4-Pro is a straightforward API change that delivers dramatically improved performance at comparable or lower cost.