Gemma 4 vs DeepSeek V4
Gemma 4 vs DeepSeek V4: multimodal edge vs million-token scale
Google's Gemma 4 and DeepSeek V4 represent two different philosophies. Gemma leads on math reasoning (89.2% AIME), multimodal vision, and edge deployment. DeepSeek leads on agentic coding (80.6% SWE-Bench) and 1M context. Here's the full breakdown.
Quick verdict
When to choose each model
Both are top-tier. The right choice depends on your primary use case.
Choose Gemma 4 when
Math reasoning, multimodal vision, edge deployment, or Apache 2.0
Gemma 4 excels at mathematical reasoning (89.2% AIME), multimodal understanding (76.9% MMMU Pro), and offers the widest deployment range from 2.3B edge models with audio to 31B flagship. Apache 2.0 license provides maximum commercial freedom.
Best for: math tutoring, document analysis, on-device AI, multimodal applications, and deployments where Apache 2.0 licensing matters.
Choose DeepSeek V4 when
Agentic coding, 1M context, or cost-efficient API
DeepSeek V4 dominates autonomous coding with 80.6% SWE-Bench Verified (vs Gemma's 52%). V4-Pro offers 1M token context with 1.6T total parameters. API pricing at $1.74/M input tokens is highly competitive.
Best for: AI coding agents, very long context tasks, cost-sensitive API deployments, and large-scale code generation.
Google DeepMind
Gemma 4 31B Dense
#3 on Arena AI. 89.2% AIME, 80% LiveCodeBench, 76.9% MMMU Pro. Dense architecture with 256K context.
30.7B parameters, all active. Best for maximum quality across reasoning, coding, and multimodal tasks.
Google DeepMind
Gemma 4 26B A4B MoE
Near-31B quality at 4B inference cost. 88.3% AIME, 77.1% LiveCodeBench. 256K context.
25.2B total, 3.8B active per token. 128 experts, 8 active + 1 shared.
DeepSeek
DeepSeek V4-Pro
80.6% SWE-Bench Verified, 83.4% BrowseComp. 1.6T total params, 49B active. 1M context window.
Massive MoE architecture with 49B active parameters per token. Dominates agentic coding and browsing benchmarks.
DeepSeek
DeepSeek V4-Flash
284B total, 13B active. 1M context. Cost-efficient at $1.74/M input tokens.
Lighter MoE variant optimized for speed and cost. Strong performance at a fraction of V4-Pro compute.
Head to head
Where each model wins
A category-by-category breakdown of strengths and weaknesses.
Math reasoning: Gemma wins
Gemma 4 31B: 89.2% AIME 2026. DeepSeek V4-Pro: ~78%. Gemma's thinking mode produces exceptional mathematical reasoning chains.
Agentic coding: DeepSeek wins
DeepSeek V4-Pro: 80.6% SWE-Bench Verified. Gemma 4: 52%. DeepSeek has a massive lead on autonomous code editing.
Browsing & web tasks: DeepSeek wins
DeepSeek V4-Pro: 83.4% BrowseComp. DeepSeek's agentic capabilities extend to web browsing and information retrieval tasks.
Multimodal: Gemma wins
Gemma 4: 76.9% MMMU Pro with native vision encoder. DeepSeek V4 is primarily text-focused. Gemma has a clear multimodal advantage.
Context window: DeepSeek wins
DeepSeek V4: 1M tokens. Gemma 4: 256K. For very long documents and codebases, DeepSeek has a 4x context advantage.
Edge deployment: Gemma wins
Gemma 4 has E2B (2.3B) and E4B (4.5B) edge models with native audio. DeepSeek V4's smallest model (284B total) is server-only.
Architecture comparison
Dense vs massive MoE: different scaling strategies
Gemma 4 offers a dense flagship and efficient MoE. DeepSeek V4 goes all-in on massive MoE scale.
Gemma 4 31B Dense
- 30.7B total parameters, all active per token
- Dense architecture for maximum quality
- 256K context window
- Native multimodal (text + image)
- Apache 2.0 license
DeepSeek V4-Pro
- 1.6T total parameters, 49B active per token
- Massive MoE with 1M context window
- 80.6% SWE-Bench Verified
- 67.9% Terminal-Bench 2.0
- MIT license, $1.74/M input tokens
Benchmarks
Complete benchmark comparison
Head-to-head benchmark results across reasoning, coding, multimodal, and agentic tasks.
Gemma leads on math reasoning and multimodal. DeepSeek leads on agentic coding and long context. The choice depends on your primary use case.
Math: Gemma 4 31B (89.2% AIME) vs DeepSeek V4-Pro (~78%) - Gemma wins by 11 points
Agentic coding: DeepSeek V4-Pro (80.6% SWE-Bench) vs Gemma 4 (52%) - DeepSeek wins by 29 points
Multimodal: Gemma 4 (76.9% MMMU Pro) - Gemma has native vision, DeepSeek is text-focused
Context: DeepSeek V4 (1M tokens) vs Gemma 4 (256K) - DeepSeek has 4x more context
Head to head
Gemma 4 vs DeepSeek V4 on key benchmarks
Direct comparison across the most important evaluation benchmarks.
| Benchmark | Gemma 4 31B Dense 31B | Gemma 4 26B MoE 4B active 26B | DeepSeek V4-Pro MoE 49B active 1.6T | DeepSeek V4-Flash MoE 13B active 284B |
|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 85.2% | 82.6% | 83.8% | 79.5% |
AIME 2026 Mathematics | 89.2% | 88.3% | 78.0% | 72.5% |
LiveCodeBench v6 Code generation | 80.0% | 77.1% | 78.5% | 73.0% |
SWE-Bench Verified Agentic coding | 52.0% | - | 80.6% | - |
BrowseComp Web browsing | - | - | 83.4% | - |
Terminal-Bench 2.0 Terminal tasks | 42.9% | - | 67.9% | - |
MMMU Pro Multimodal | 76.9% | 73.8% | - | - |
Arena AI ELO Human preference | 1452 | 1441 | - | - |
Context Window Max tokens | 256K | 256K | 1M | 1M |
Active params Per token | 30.7B | 3.8B | 49B | 13B |
License Commercial use | Apache 2.0 | Apache 2.0 | MIT | MIT |
Data from official model cards and independent evaluations. Scores may vary by evaluation methodology.
Coding
The coding gap: DeepSeek V4 dominates agentic tasks
DeepSeek V4-Pro's 80.6% on SWE-Bench Verified is one of the highest scores among open models. Gemma 4 holds its own on code generation (LiveCodeBench) but trails significantly on autonomous editing.
- Agentic coding: DeepSeek V4-Pro 80.6% vs Gemma 4 52% (SWE-Bench Verified)
- Code generation: Gemma 4 80% vs DeepSeek V4-Pro 78.5% (LiveCodeBench v6)
- Terminal tasks: DeepSeek V4-Pro 67.9% vs Gemma 4 42.9% (Terminal-Bench 2.0)
Reasoning & Vision
Math reasoning and multimodal: Gemma 4's strongest areas
Gemma 4's 89.2% on AIME 2026 significantly outperforms DeepSeek V4. Combined with native multimodal vision (76.9% MMMU Pro), Gemma 4 is the stronger choice for reasoning and visual understanding tasks.
- AIME 2026: Gemma 4 89.2% vs DeepSeek V4-Pro ~78%
- Multimodal: Gemma 4 76.9% MMMU Pro - native vision encoder
- DeepSeek V4 is primarily text-focused without native vision
Deployment & Cost
Edge models vs API cost efficiency
Gemma 4 covers edge to cloud with models from 2.3B to 31B, all under Apache 2.0. DeepSeek V4 offers competitive API pricing ($1.74/M input) and 1M context, but requires server-grade hardware for self-hosting.
- Gemma 4: E2B (2.3B), E4B (4.5B), 26B MoE, 31B Dense - all Apache 2.0
- DeepSeek V4: $1.74/M input, $3.48/M output - competitive API pricing
- Only Gemma 4 has edge models with native audio support
Try both
Test the models yourself
The best comparison is hands-on experience.
Gemma 4 resources
Get started with Gemma 4
Everything you need to start building with Gemma 4.
DeepSeek V4 resources
Learn more about DeepSeek V4
Official DeepSeek V4 resources and documentation.
Open model landscape
The best open models of 2026
Gemma 4 and DeepSeek V4 are two of the most capable open models, but they're not the only options.
Try Gemma 4
Experience Gemma 4's strengths firsthand
Try Gemma 4 for free and see how it performs on your specific tasks. Math reasoning, multimodal vision, and edge deployment are where it shines brightest.