DeepSeek V4 vs Gemma 4
DeepSeek V4 vs Gemma 4: trillion-scale coding vs multimodal edge AI
DeepSeek V4-Pro delivers 80.6% on SWE-bench Verified with 1.6 trillion parameters and a one-million-token context window. Google Gemma 4 scores 89.2% on AIME 2026 math reasoning, includes native vision and audio, and runs locally on 18GB RAM. Two fundamentally different approaches to open AI: maximum coding performance at scale versus multimodal intelligence on consumer hardware.
Head to head
DeepSeek V4 vs Gemma 4 across key categories
These models occupy different weight classes. V4-Pro has 1.6 trillion parameters. Gemma 4-31B has 31 billion. The comparison is not about which is bigger, but which solves your specific problem better.
Agentic coding: DeepSeek V4 dominates by a wide margin
DeepSeek V4-Pro Max scores 80.6% on SWE-bench Verified. Gemma 4-31B reaches approximately 52 to 64% depending on the evaluation harness. On Terminal-Bench 2.0, V4-Pro Max hits 67.9% compared to Gemma 4-31B at 42.9%. For autonomous code editing, bug fixing, and repository-level engineering tasks, V4-Pro has a commanding lead.
Math reasoning: Gemma 4 outperforms on AIME
Gemma 4-31B scores 89.2% on AIME 2026, significantly ahead of V4-Pro at approximately 78%. Gemma 4's thinking mode produces strong mathematical reasoning chains. On MMLU, both are competitive: Gemma 4-31B at 87% and V4-Pro at 90.1%. For math tutoring, scientific analysis, and formal reasoning tasks, Gemma 4 has a clear advantage.
Multimodal: Gemma 4 includes native vision and audio
All Gemma 4 variants support native image understanding. The E2B and E4B edge models also handle audio and video input. DeepSeek V4 is a text-only preview without native multimodal capabilities. For workflows involving document scanning, image analysis, visual question answering, or audio transcription, Gemma 4 is the only option in this comparison.
Context window: DeepSeek V4 offers four times more
DeepSeek V4 supports one million tokens. Gemma 4 supports 256K tokens. For processing entire codebases, very long legal documents, or extended research papers in a single pass, V4's context advantage is significant. Gemma 4's 256K window covers most practical use cases but cannot match V4 for truly massive inputs.
Edge and mobile deployment: only Gemma 4 fits
Gemma 4 E2B (2.3 billion parameters) runs on 4GB RAM, suitable for mobile phones. E4B (4.5 billion) runs on 8GB, suitable for laptops. The 26B MoE runs on 18GB with quantization, fitting a single RTX 4090 or MacBook M4 Pro. DeepSeek V4 has no edge variant. The smallest model (V4-Flash at 284B total) is server-only. For on-device AI, Gemma 4 is the only choice.
Code generation quality: closer than SWE-bench suggests
On LiveCodeBench (competitive code generation), Gemma 4-31B scores 80.0% and V4-Pro Max scores 93.5. On HumanEval (function-level coding), Gemma 4-31B reaches approximately 88% and V4-Pro approximately 90%. The gap narrows on simpler coding tasks. SWE-bench measures repository-level autonomous editing, where V4's massive context and parameter count provide a structural advantage.
API pricing: comparable input costs, different output costs
DeepSeek V4-Flash costs $0.14 per million input tokens and $0.28 output. Gemma 4-31B on OpenRouter costs $0.14 input and $0.40 output. Input pricing is identical. V4-Flash is 30% cheaper on output. V4-Pro at $1.74 input and $3.48 output is significantly more expensive but delivers much higher benchmark scores.
Licensing: both are fully permissive
Gemma 4 uses Apache 2.0. DeepSeek V4 uses MIT. Both allow unrestricted commercial use, modification, and redistribution. Apache 2.0 includes a patent grant, which some legal teams prefer. MIT is simpler. For practical purposes, both licenses provide equivalent commercial freedom.
Quick verdict
When to choose DeepSeek V4 vs Gemma 4
Different models for different deployment realities.
Choose DeepSeek V4 when
- You need the highest autonomous coding performance (80.6% SWE-bench)
- Your documents or codebases exceed 256K tokens
- You want adjustable reasoning depth (Non-think, Think High, Think Max)
- Cost-efficient API access matters (V4-Flash at $0.14 per M input)
- You need broad programming language support (338 languages)
Choose Gemma 4 when
- You need to run AI on phones, laptops, or consumer GPUs
- Your workflow involves images, audio, or video understanding
- Math reasoning quality is your primary concern (89.2% AIME)
- You want the widest model size range (2B to 31B)
- Apache 2.0 licensing with patent grant is preferred
Benchmarks
DeepSeek V4 vs Gemma 4 benchmark comparison
Complete benchmark results across coding, reasoning, multimodal, and deployment specifications. V4 leads on coding and context. Gemma 4 leads on math, multimodal, and edge deployment.
DeepSeek V4 and Gemma 4 represent two distinct philosophies in open AI. V4-Pro scales to 1.6 trillion parameters with 49 billion active, targeting maximum performance on coding and agentic tasks. Gemma 4-31B Dense uses all 31 billion parameters with no MoE routing, achieving strong reasoning quality at a fraction of the compute. The 26B MoE variant activates only 3.8 billion parameters per token, enabling local deployment on consumer hardware. The benchmark table below covers the evaluations that matter most for choosing between these two families.
Coding: V4-Pro Max 80.6% SWE-bench vs Gemma 4-31B approximately 52 to 64%
Math: Gemma 4-31B 89.2% AIME vs V4-Pro approximately 78%
Multimodal: Gemma 4 76.9% MMMU Pro with native vision, V4 is text-only
Context: V4 supports 1M tokens, Gemma 4 supports 256K
Edge: Gemma 4 E2B runs on 4GB RAM, V4 has no edge variant
API input cost: both V4-Flash and Gemma 4-31B at $0.14 per M tokens
Full comparison
DeepSeek V4 family vs Gemma 4 family
Head-to-head results across the most important evaluation benchmarks.
| Benchmark | V4-Pro Max 1.6T / 49B active Frontier | V4-Flash Max 284B / 13B active Efficient | Gemma 4 31B Dense Reasoning | Gemma 4 26B MoE 4B active Local |
|---|---|---|---|---|
SWE-bench Verified Autonomous code editing | 80.6% | 79.0% | ~52-64% | - |
LiveCodeBench Code generation | 93.5 | 91.6 | 80.0 | 77.1 |
HumanEval Function-level coding | ~90% | - | ~88% | - |
Terminal-Bench 2.0 Terminal operations | 67.9% | 56.9% | 42.9% | - |
AIME 2026 Mathematics | ~78% | - | 89.2% | 88.3% |
MMLU General knowledge | 90.1% | 88.7% | 87% | - |
GPQA Diamond Scientific reasoning | 90.1% | 88.1% | 78% | - |
MMMU Pro Multimodal understanding | - | - | 76.9% | 73.8% |
Context window Maximum tokens | 1M | 1M | 256K | 256K |
Active parameters Per token | 49B | 13B | 31B | 3.8B |
Minimum hardware For local inference | GPU cluster | 2x H100 | 1x H100 | 18GB VRAM |
License Commercial use | MIT | MIT | Apache 2.0 | Apache 2.0 |
Data from official model cards. DeepSeek V4 (April 2026), Gemma 4 (April 2026). Some scores vary by evaluation methodology.
Coding
DeepSeek V4 leads autonomous coding by a significant margin
The gap between V4-Pro and Gemma 4 on SWE-bench Verified is roughly 17 to 29 percentage points, depending on the Gemma evaluation. This reflects a fundamental architectural difference: V4-Pro activates 49 billion parameters per token with a one-million-token context window, giving it the capacity to reason across entire repositories. Gemma 4-31B is strong on isolated code generation tasks but lacks the scale for complex multi-file autonomous editing.
- SWE-bench Verified: V4-Pro Max 80.6% vs Gemma 4-31B approximately 52 to 64%
- LiveCodeBench: V4-Pro Max 93.5 vs Gemma 4-31B 80.0
- Terminal-Bench 2.0: V4-Pro Max 67.9% vs Gemma 4-31B 42.9%
- V4 supports 338 programming languages
Edge and multimodal
Gemma 4 covers phones to workstations with native vision
Gemma 4 is the only model family in this comparison that runs on mobile devices. The E2B variant needs just 4GB RAM. The 26B MoE runs on a single RTX 4090 or MacBook M4 Pro at 18 to 35 tokens per second. All variants include native image understanding, and the edge models add audio and video support. For teams building on-device AI products, Gemma 4 has no open-weight competitor.
- E2B: 2.3B parameters, 4GB RAM, mobile and embedded
- E4B: 4.5B parameters, 8GB RAM, laptops and browsers
- 26B MoE: 3.8B active, 18GB RAM, consumer GPU
- 31B Dense: full quality, single H100 or high-end workstation
Long context
DeepSeek V4 processes four times more context than Gemma 4
V4's one-million-token context window is four times larger than Gemma 4's 256K limit. The hybrid attention architecture (CSA plus HCA) reduces inference FLOPs to 27% and KV cache to 10% compared to standard attention at the same context length. This means V4 can process entire large codebases, full legal contracts, or comprehensive research papers without chunking or retrieval augmentation.
- V4: 1M tokens with 27% FLOPs and 10% KV cache vs standard attention
- Gemma 4: 256K tokens, sufficient for most documents
- V4 Think Max mode recommends at least 384K tokens for reasoning budget
- Gemma 4 26B MoE: 256K context but KV cache limits practical use on consumer hardware
Related pages
Explore DeepSeek V4 models and comparisons
Learn more about the models in this comparison and how they fit into the broader open AI landscape.
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Test DeepSeek V4 coding and reasoning on your own tasks
Open the chat interface and try V4-Pro or V4-Flash on a real coding problem, long document, or analysis task. The best comparison is hands-on experience with your actual workload.