Gemma 4 vs Qwen 3.6 vs DeepSeek V4: Model Showdown
Gemma 4 vs Qwen 3.6 vs DeepSeek V4: Model Showdown
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Quick Answer: Three open-source LLMs dominate mid-2026: Gemma 4 27B (Google, best for coding/agents), Qwen 3.6 72B (Alibaba, best all-rounder, multilingual, instruction-following), and DeepSeek V4 236B-MoE (best raw intelligence, MMLU 91.2%, but needs a cluster). Pick: Gemma 4 for single-GPU deployments (fits on 24 GB VRAM), Qwen 3.6 for best quality-per-hardware ratio (2x 3090s), DeepSeek V4 for maximum capability (if you have 8x H100s). All three are Apache 2.0 or permissively licensed.
Model Overview Comparison
| Specification | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|
| Developer | Google DeepMind | Alibaba Cloud | DeepSeek (China) |
| Release Date | March 2026 | May 2026 | February 2026 |
| Parameters | 27B (dense) | 72B (dense) | 236B (MoE, 36B active) |
| Architecture | Dense Transformer | Dense Transformer | Mixture of Experts (MoE) |
| Context Length | 32K tokens | 128K tokens | 128K tokens |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Training Data | 12T tokens | 18T tokens | 14.8T tokens |
| Languages | 60+ | 100+ | 50+ |
| Open Weights | ✅ (Full) | ✅ (Full) | ✅ (Full) |
| Quantized Available | ✅ (GGUF, AWQ, GPTQ) | ✅ (GGUF, AWQ, GPTQ) | ✅ (GGUF, AWQ) |
| Primary Strength | Efficiency, coding | Versatility, multilingual | Raw intelligence |
Benchmark Results: Head-to-Head
Academic Benchmarks
| Benchmark | What It Tests | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B | Best Score |
|---|---|---|---|---|---|
| MMLU | General knowledge (57 subjects) | 84.3% | 87.8% | 91.2% | DeepSeek |
| MMLU-Pro | Harder MMLU (10 subjects, harder) | 68.5% | 73.9% | 79.1% | DeepSeek |
| GSM8K | Grade-school math | 90.1% | 94.2% | 96.7% | DeepSeek |
| MATH | Competition math (Hendrycks) | 62.4% | 72.5% | 80.3% | DeepSeek |
| HumanEval | Python code generation | 78.2% | 82.5% | 88.4% | DeepSeek |
| MBPP | Python code (basic) | 82.1% | 85.8% | 90.2% | DeepSeek |
| HellaSwag | Commonsense reasoning | 87.9% | 90.4% | 89.8% | Qwen |
| ARC-C | Science reasoning | 89.2% | 91.5% | 93.8% | DeepSeek |
| TruthfulQA | Truthfulness | 51.2% | 58.7% | 55.3% | Qwen |
| BBH | Complex reasoning | 76.8% | 83.1% | 88.4% | DeepSeek |
Chat & Instruction Benchmarks
| Benchmark | What It Tests | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|---|
| MT-Bench | Multi-turn conversation quality | 8.21 | 8.74 | 8.92 (on par with GPT-4o) |
| AlpacaEval 2.0 | Instruction-following (LC win rate) | 38.5% | 52.1% | 49.3% |
| Arena ELO (Chatbot Arena) | Elo rating from human preferences | 1,180 | 1,245 | 1,278 |
| Refusals (safety) | Appropriate refusal rate | 87% | 82% | 79% |
Performance Per Parameter
| Metric | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B (MoE) |
|---|---|---|---|
| MMLU per billion params | 3.12% | 1.22% | 2.53% (active params: 1.91%) |
| HumanEval per billion params | 2.90% | 1.15% | 2.46% (active params: 1.86%) |
| Tokens per second (FP16, 1x H100) | ~85 tok/s | ~32 tok/s | ~14 tok/s (quantized: ~55 tok/s) |
| VRAM required (FP16) | 54 GB | 144 GB | 475 GB (36 GB active w/ MoE) |
| VRAM required (INT4) | 16 GB | 42 GB | 140 GB (MoE offloading) |
Efficiency Winner: Gemma 4. For its size, it punches way above its weight. Gemma 4 27B gets 84.3% MMLU — close to Qwen 3.6 72B (87.8%) but with 60% fewer parameters. DeepSeek V4 wins on absolute performance but needs a cluster.
Benchmarks Visualization
MMLU
Gemma 4 27B ████████████████████████████████████████░░░░ 84.3
Qwen 3.6 72B █████████████████████████████████████████████ 87.8
DeepSeek V4 ███████████████████████████████████████████████ 91.2
HumanEval
Gemma 4 27B █████████████████████████████████████████░░░ 78.2
Qwen 3.6 72B █████████████████████████████████████████████ 82.5
DeepSeek V4 ████████████████████████████████████████████████ 88.4
MT-Bench
Gemma 4 27B ████████████████████████████████████████░░░░ 8.21
Qwen 3.6 72B █████████████████████████████████████████████ 8.74
DeepSeek V4 ███████████████████████████████████████████████ 8.92
Hardware Requirements & Inference Cost
Minimum Hardware for Local Deployment
| Model | FP16 VRAM | INT4 VRAM | Minimum GPU | Practical Setup |
|---|---|---|---|---|
| Gemma 4 27B | 54 GB | 16 GB | 1x A100-80GB (FP16) or 1x 3090/4090 (INT4) | 1x 24 GB GPU (INT4) — easiest to run |
| Qwen 3.6 72B | 144 GB | 42 GB | 2x A100-80GB (FP16) or 1x A100-80GB (INT4) | 2x 3090/4090 (INT4) or 1x H100 |
| DeepSeek V4 236B | 475 GB (full) | 140 GB | 4x H100 (FP16 full) or 2x H100 (INT4) | 4-8x consumer GPUs or cloud cluster |
Inference Cost Comparison (Cloud API)
| Provider | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|
| Together.ai | $0.18/M tokens | $0.54/M tokens | $1.20/M tokens |
| Groq | $0.15/M tokens | $0.45/M tokens | N/A (too large) |
| Fireworks | $0.12/M tokens | $0.48/M tokens | $1.10/M tokens |
| Self-hosted (cost per 1M tokens) | $0.02-$0.05 | $0.08-$0.15 | $0.20-$0.50 |
| Latency (first token) | 150ms | 350ms | 800ms |
| Best for budget | ✅ Cheapest per token | ✅ Moderate | ❌ Expensive |
Self-Hosted Cost: Total Cost of Ownership
Scenario: 10M tokens/day inference, 30 days
| Model | GPU Setup (cost) | Power/Colo | Cloud API Cost | Self-Hosted Cost | Savings vs API |
|---|---|---|---|---|---|
| Gemma 4 27B (INT4) | 1x 3090 ($800 used) | $0.50/hr | $540/mo | $360/mo | 33% |
| Gemma 4 27B (FP16) | 1x A100 ($10K) | $1.50/hr | $540/mo | $1,080/mo | -100% (cheaper via API) |
| Qwen 3.6 72B (INT4) | 2x 3090 ($1.6K) | $1.00/hr | $1,620/mo | $720/mo | 56% |
| DeepSeek V4 (AWS) | 2x H100 ($8K/mo rent) | Included | $3,600/mo | $8,000/mo | -122% (cheaper via API) |
Key insight: For low-volume (< 10M tok/day), API calls are cheaper than self-hosting. For high-volume (> 100M tok/day), self-hosting Gemma 4 or Qwen 3.6 pays off. DeepSeek V4 is almost always cheaper via API due to massive hardware requirements.
Photo by Markus Winkler on Pexels
Coding Performance
Language-Specific Benchmarks
| Task | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B | Winner |
|---|---|---|---|---|
| Python (HumanEval+) | 78.2% | 82.5% | 88.4% | DeepSeek |
| TypeScript | 72.4% | 76.9% | 82.1% | DeepSeek |
| Rust | 65.1% | 69.8% | 77.3% | DeepSeek |
| SQL | 67.8% | 74.2% | 79.5% | DeepSeek |
| Bash | 70.3% | 73.1% | 76.8% | DeepSeek |
| SWE-Bench Verified | 38.5% | 45.2% | 52.1% | DeepSeek |
| Repo-level coding | 34.2% | 40.8% | 47.6% | DeepSeek |
| Code review/explanation | 8.5/10 | 8.9/10 | 9.1/10 | DeepSeek |
Coding-Specific Strengths
Gemma 4 27B:
- Very fast code completion (best for IDE copilot replacement)
- Good at generating short, precise functions
- Excellent with Google Cloud / Go / Python stack
- Fits on a 3090 for local coding assistant
- Fastest token generation (85 tok/s) = lowest latency
Qwen 3.6 72B:
- Strongest at complex multi-file refactoring
- Best at understanding project-wide context (128K context)
- Excellent at writing tests and documentation
- Strong TypeScript/React ecosystem support
- Good balance of quality and speed
DeepSeek V4:
- Best at solving novel algorithmic challenges
- Most capable at repository-level understanding
- Excels at complex debugging scenarios
- Best at generating idiomatic Rust
- Highest SWE-Bench score (52.1% vs GPT-4o's ~45%)
Multilingual & Reasoning
Multilingual Benchmark
| Language | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|
| English | 87.2 | 89.5 | 92.1 |
| Chinese (Simplified) | 78.5 | 93.2 | 88.7 |
| Chinese (Traditional) | 75.8 | 90.1 | 85.4 |
| Japanese | 79.2 | 86.4 | 82.1 |
| Korean | 76.5 | 84.8 | 80.3 |
| Spanish | 82.1 | 85.3 | 86.1 |
| French | 81.5 | 84.2 | 85.3 |
| German | 80.8 | 83.7 | 84.9 |
| Arabic | 72.4 | 79.8 | 75.6 |
| Hindi | 74.1 | 81.2 | 77.3 |
Multilingual Winner: Qwen 3.6 — trained on 100+ languages with strong all-around performance. Chinese and East Asian languages are notably better. Gemma 4 is best at European languages. DeepSeek V4 strong in English but Chinese-focused training data shows.
Reasoning Capabilities
| Reasoning Type | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|
| Chain-of-thought | Good | Very Good | Excellent |
| Multi-step math | 72.4% | 81.5% | 88.2% |
| Logical deduction | 78.8% | 85.3% | 90.1% |
| Scientific reasoning | 80.2% | 83.9% | 89.5% |
| Spatial reasoning | 68.5% | 73.2% | 79.8% |
| Contradiction detection | 82.1% | 86.4% | 91.2% |
| Tool-use planning | 79.5% | 84.8% | 87.3% |
Specialization & Fine-Tuning
Fine-Tuning Difficulty
| Factor | Gemma 4 27B | Qwen 3.6 72B | DeepSeek V4 236B |
|---|---|---|---|
| QLoRA VRAM | 16 GB (1x 3090/4090) | 48 GB (2x 3090 or 1x A100) | 80 GB (1x H100) |
| Fine-tuning time (1 epoch) | 2-4 hours (1x 3090) | 8-12 hours (2x 3090) | 24-48 hours (8x H100) |
| LoRA target modules | q_proj, v_proj, o_proj | q_proj, k_proj, v_proj, o_proj | All attention + expert modules |
| Merge difficulty | Easy | Moderate | Hard (MoE merging complex) |
| Dataset prep ease | Easy | Easy | Hard (needs careful formatting) |
| Best tools | Unsloth, Axolotl | Axolotl, LLaMA-Factory | LLaMA-Factory, vLLM |
Recommended Use Cases for Fine-Tuning
Gemma 4 27B:
- ✅ Single-GPU fine-tuning (lowest barrier)
- ✅ Domain-specific smaller models (legal, medical, finance)
- ✅ Fast prototyping of custom behaviors
- ✅ Deploying to edge/mobile (smallest quantized at ~8 GB)
- ❌ Complex tasks requiring deep reasoning
Qwen 3.6 72B:
- ✅ Enterprise fine-tuning (good quality per hardware cost)
- ✅ Multilingual domain adaptation
- ✅ Best choice for most business applications
- ✅ Code generation for custom internal tooling
- ❌ Budget-constrained (needs multi-GPU)
DeepSeek V4 236B:
- ✅ Cutting-edge fine-tuning research
- ✅ Maximum quality (if you can afford it)
- ✅ Complex agentic behaviors
- ✅ Institutional/research applications
- ❌ Most teams (infrastructure cost is prohibitive)
When to Pick Which Model
Decision Matrix
Your situation:
┌─────────────────────────────────────────────────────────┐
│ Your primary need: │
├─────────────────────────────────────────────────────────┤
│ Coding assistant → Gemma 4 27B (fast, fits on 1 GPU) │
│ General chatbot → Qwen 3.6 72B (best all-round) │
│ Raw intelligence → DeepSeek V4 236B (cluster required) │
│ Multilingual → Qwen 3.6 72B (100+ languages) │
│ Budget limit (<$500/mo) → Gemma 4 27B (API or 1x 3090) │
│ Enterprise QA → Qwen 3.6 72B (good speed+quality) │
│ Research/Advanced → DeepSeek V4 (best benchmarks) │
│ Single GPU only → Gemma 4 27B (no question) │
│ 2x 3090s available → Qwen 3.6 72B (INT4) │
│ 8x H100 cluster → DeepSeek V4 (max capability) │
└─────────────────────────────────────────────────────────┘
Quick Selection
| If you have | Pick | Why |
|---|---|---|
| 1x 3090/4090 (24 GB) | Gemma 4 27B (INT4) | Only option that fits |
| 2x 3090/4090 | Qwen 3.6 72B (INT4) | Best quality for this setup |
| 1x A100 (80 GB) | Qwen 3.6 72B (INT4) | Fast, efficient, high quality |
| 1x H100 | Qwen 3.6 72B (FP16) or DeepSeek V4 (quantized) | Depends on quality vs speed |
| 4x H100+ | DeepSeek V4 236B | Full capability |
| API budget only | Gemma 4 27B or Qwen 3.6 72B | 3-6x cheaper than DeepSeek |
| Need speed | Gemma 4 27B | Fastest inference (85 tok/s) |
| Need max quality | DeepSeek V4 | Best bench scores |
Related Reads
- DeepSeek V4 vs Qwen 3.5 vs Llama 4: Best Open-Source LLM
- Best Open-Source LLM 2026: 8 Models Compared
- Vision-Language Models: Architecture, Training, and Multimodal Applications
Key Takeaways
- For single-GPU deployments (e.g., 1x 3090/4090), Gemma 4 27B (INT4) is the only viable option—fits in 16 GB VRAM with 85 tokens/sec throughput and excels at coding/IDE copilot tasks.
- Qwen 3.6 72B delivers the best quality-per-hardware ratio: 2x 3090s (INT4) or 1x A100 (FP16) unlocks 87.8% MMLU and 128K context, ideal for multilingual (100+ languages) or enterprise QA use cases.
- DeepSeek V4 236B-MoE leads in raw intelligence (91.2% MMLU, 52.1% SWE-Bench) but requires 4-8x H100s or cloud APIs—costs $1.20/M tokens vs. $0.12/M for Gemma 4, making it impractical for most teams.
- Self-hosting Gemma 4 or Qwen 3.6 pays off at <100M tokens/month: Gemma 4 on a used 3090 ($800) costs $360/mo vs. $540/mo via API, while Qwen 3.6 on 2x 3090s saves 56% over API costs.
- Fine-tuning: Gemma 4 (1x 3090, 2-4 hours/epoch) is best for fast prototyping; Qwen 3.6 (2x 3090s, 8-12 hours) balances quality and cost; DeepSeek V4 (8x H100s, 24-48 hours) is reserved for institutional research.
- For coding: Gemma 4 (fastest latency, 78.2% HumanEval) beats Qwen 3.6 (82.5%) for IDE copilots, while DeepSeek V4 (88.4%) dominates algorithmic challenges and repo-level tasks—pick based on latency vs. complexity needs.
Frequently Asked Questions
Which model is best for coding in 2026?
DeepSeek V4 is the strongest coder overall (88.4% HumanEval, 52.1% SWE-Bench). But for practical use: Gemma 4 27B is the best coding "copilot" replacement — it fits on a single GPU, has <200ms latency, and covers most day-to-day coding needs. Qwen 3.6 72B is best for complex multi-file projects and refactoring.
Can I run these models on a consumer GPU?
Only Gemma 4 27B runs well on consumer GPUs (1x 3090/4090 with INT4 quantization, ~16 GB VRAM). Qwen 3.6 72B needs 2x 3090s (INT4) or an A100/H100. DeepSeek V4 needs multiple H100s or a cloud cluster. For consumer hardware, Gemma 4 is the clear choice.
Which model has the best price-to-performance ratio?
Qwen 3.6 72B offers the best balance. It's 2-3x more capable than Gemma 4 on hard benchmarks but only requires 2x consumer GPUs (vs 1x for Gemma 4). At $0.48-0.54/M tokens via API, it's affordable. DeepSeek V4 is better but costs 3x more per token.
Are all three models truly open-source?
All three use Apache 2.0 licenses with open weights. No restrictions on commercial use, fine-tuning, or redistribution. DeepSeek V4 has the most permissive license (no use-case restrictions). Qwen 3.6 is Apache 2.0 but Alibaba's terms recommend attribution. Gemma 4 is fully Apache 2.0 with no extra clauses.
Which model should I fine-tune?
If you have one GPU: Gemma 4 27B (QLoRA on 1x 3090). If you have 2+ GPUs: Qwen 3.6 72B (better base model to start from). Only fine-tune DeepSeek V4 if you have institutional-grade infrastructure and need the absolute best quality.
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