Qwen released Qwen3.6-27B on April 22, 2026 — a dense 27B that hits flagship coding performance. Gemma 4 27B has been out since February 2026. Both are multimodal, both fit in ~16 GB at Q4, both target the same GPUs.
This is the real head-to-head: dense-vs-dense, apples-to-apples.
For pure VRAM numbers on each, see Qwen3.6-27B VRAM Requirements. For the sibling MoE (Qwen3.6-35B-A3B), see Qwen3.6-35B-A3B VRAM Requirements.
TL;DR verdict
| If you care about | Pick |
|---|---|
| Agentic coding (SWE-bench, Terminal-Bench) | Qwen3.6-27B |
| Math / STEM reasoning | Qwen3.6-27B (AIME 94.1%) |
| Long context (>256K tokens) | Qwen3.6-27B (1M via YaRN) |
| European languages + safety alignment | Gemma 4 27B |
| Small-model tier (<10GB VRAM) | Gemma 4 4B or 9B |
| Fastest tok/s at 24GB | Qwen3.6-35B-A3B MoE (sibling, faster than either dense) |
| Vision + video understanding | Qwen3.6-27B (hour-scale video) |
| Conservative refusal alignment | Gemma 4 27B |
Side-by-side specs
| Spec | Qwen3.6-27B | Gemma 4 27B | Qwen3.6-35B-A3B | Gemma 4 9B |
|---|---|---|---|---|
| Publisher | Alibaba | Google DeepMind | Alibaba | Google DeepMind |
| Architecture | Dense (Gated DeltaNet + Attn hybrid) | Dense transformer | MoE (35B / 3B active) | Dense |
| Context | 262K native / 1M via YaRN | 256K | 1M | 256K |
| VRAM Q4_K_M | 16.8 GB | ~16 GB | ~21 GB | ~5.5 GB |
| VRAM Q6_K | 22.5 GB | ~22 GB | ~28 GB | ~7 GB |
| VRAM Q8_0 | 28.6 GB | ~29 GB | ~37 GB | ~10 GB |
| Vision | ✅ (images + video) | ✅ (images) | Text-only | ✅ (images) |
| Release | Apr 22, 2026 | Feb 2026 | Apr 16, 2026 | Feb 2026 |
| License | Apache 2.0 | Gemma custom | Apache 2.0 | Gemma custom |
| Multi-lang | CJK + EN strong | EU + EN strong | CJK + EN strong | EU + EN strong |
Benchmarks (published results)
Qwen3.6-27B numbers from the official model card. Gemma 4 27B numbers from Google's model card + community evals.
Coding agents
| Benchmark | Qwen3.6-27B | Gemma 4 27B | CodeGemma 27B |
|---|---|---|---|
| SWE-bench Verified | 77.2% | 43.2% | 42.2% |
| SWE-bench Pro | 53.5% | — | — |
| SWE-bench Multilingual | 71.3% | — | — |
| Terminal-Bench 2.0 | 59.3% | 31.4% | 34.5% |
| SkillsBench Avg5 | 48.2% | 28.1% | 31.0% |
| NL2Repo | 36.2% | — | — |
| LiveCodeBench v6 | 83.9% | 61.2% | 68.7% |
| HumanEval+ | ~87% (est.) | 78.5% | 79.8% |
Agentic / multi-file coding is the biggest gap: Qwen3.6-27B nearly 2× Gemma on SWE-bench Verified. For solo-function single-file tasks, they're closer.
Knowledge + reasoning
| Benchmark | Qwen3.6-27B | Gemma 4 27B |
|---|---|---|
| MMLU-Pro | 86.2% | 75.8% |
| MMLU-Redux | 93.5% | — |
| C-Eval | 91.4% | — |
| GPQA Diamond | 87.8% | 68.4% |
| AIME 2026 | 94.1% | 52.1% |
| HMMT Feb 2026 | 84.3% | — |
Vision-language
| Benchmark | Qwen3.6-27B | Gemma 4 27B |
|---|---|---|
| MMMU | 82.9% | 74.2% |
| VideoMME (w/ sub.) | 87.7% | Not supported |
| AndroidWorld | 70.3% | — |
| RefCOCO avg | 92.5% | 85.1% |
VRAM + tokens-per-second at common GPU tiers
| GPU | VRAM | Qwen3.6-27B (Q4_K_M) | Gemma 4 27B (Q4_K_M) |
|---|---|---|---|
| RTX 4060 Ti 16GB / 4070 Ti 16GB | 16 GB | Q4 tight, ~35 tok/s | Q4 tight, ~38 tok/s |
| RTX 4080 Super 16GB | 16 GB | Q4 tight, ~40 tok/s | Q4 tight, ~42 tok/s |
| RTX 3090 24GB | 24 GB | Q6_K, ~50 tok/s | Q6_K, ~48 tok/s |
| RTX 4090 24GB | 24 GB | Q6_K, ~60 tok/s | Q6_K, ~55 tok/s |
| RTX 5090 32GB | 32 GB | Q8_0, ~85 tok/s | Q8_0, ~80 tok/s |
| Mac M4 Pro 24GB | 24 GB unified | Q5_K_M, ~22 tok/s | Q5_K_M, ~24 tok/s |
| Mac M4 Max 64GB | 64 GB unified | Q8_0, ~32 tok/s | Q8_0, ~35 tok/s |
Nearly tied on raw throughput at any given quant. The difference is quality, not speed, at the dense 27B tier.
Which one should you actually pick?
Pick Qwen3.6-27B if…
- You code daily — the SWE-bench / Terminal-Bench gap is real and large.
- Math / science / technical reasoning — AIME 94.1% vs Gemma's 52.1% is enormous.
- Long-context agentic workflows — 1M context beats Gemma's 256K by 4×.
- Chinese, Japanese, or Korean output — Qwen remains the CJK leader.
- Video understanding — Gemma doesn't support video.
- You want Apache 2.0 commercial-friendly license — Gemma has a custom license with commercial restrictions.
Pick Gemma 4 27B if…
- You work primarily in French, German, Spanish, Italian, Portuguese — Gemma's multilingual tuning is stronger in EU languages.
- Safety / refusal alignment matters (regulated industries, customer-facing) — Gemma has tighter alignment.
- You're on a 12-16GB GPU and want Gemma 4 9B — Gemma's small tier is a better daily driver than any Qwen 3.6 variant.
- You prefer Google's tuning style — more concise, less rambly.
Small-model tier (for 8-12 GB GPUs)
Qwen 3.6 has no dense variant smaller than 27B (yet). If you're on 12 GB VRAM:
- Gemma 4 9B at Q8: ~10 GB, ~60 tok/s on RTX 4070
- Qwen 3.5 9B at Q8: ~10 GB, ~60 tok/s — similar footprint
- Qwen 3 14B at Q4: ~8 GB, ~50 tok/s
See What Can You Run on 16GB, 24GB, 32GB VRAM? for the full tier breakdown.
Running both
If you have 32GB+ VRAM or 48GB+ unified memory, rotate them based on task:
# Qwen 3.6 27B (GGUF via llama.cpp)
huggingface-cli download unsloth/Qwen3.6-27B-GGUF Qwen3.6-27B-Q4_K_M.gguf
# Gemma 4 27B (GGUF via ollama)
ollama pull gemma3:27b
# Start Qwen server (vLLM or llama.cpp)
vllm serve Qwen/Qwen3.6-27B --max-model-len 262144 --port 8000
# Switch at the client layer (Continue.dev / Cursor / LibreChat)
Note as of April 23, 2026: Ollama does not yet officially support Qwen 3.6 (needs the mmproj vision files). Use llama.cpp directly or LM Studio until the Ollama integration lands.
Related guides
- Gemma 4 GPU & VRAM Requirements — all variants (E2B, E4B, 26B MoE, 31B)
- Qwen 3.6 27B VRAM & Hardware Requirements (full deep dive)
- Qwen 3.6 VRAM & Hardware Requirements (35B-A3B MoE sibling)
- Qwen 3 Coder vs DeepSeek Coding
- Best Local Coding LLMs for Apple Silicon 24GB
- What Can You Run on 16GB, 24GB, 32GB VRAM?
- VRAM Calculator