Qwen 3.6 27B or Gemma 4 27B — which is better for coding?

Qwen3.6-27B wins on coding. It scores SWE-bench Verified 77.2%, Terminal-Bench 2.0 59.3%, SkillsBench 48.2% — matching or beating the previous-gen Qwen3.5-397B-A17B MoE with only 27B dense parameters. Gemma 4 27B scores ~78% on HumanEval+ but lags on agentic coding benchmarks that measure multi-file / tool-use reasoning.

Which has longer context — Qwen 3.6 27B or Gemma 4 27B?

Qwen3.6-27B wins decisively: 262K native extensible to 1,010,000 tokens via YaRN. Gemma 4 27B caps at 256K. For 1M-document workflows or agentic coding sessions with many tool calls, Qwen 3.6 is the only realistic open-weight pick at this size.

How much VRAM does each need?

Qwen3.6-27B Q4_K_M: 16.8 GB (Unsloth GGUF). Gemma 4 27B Q4_K_M: ~16 GB. Nearly identical memory footprint. Both fit comfortably on RTX 4090 24GB or Mac M4 Pro 24GB at Q4-Q5.

Is Qwen 3.6 27B multimodal like Gemma 4?

Yes. Qwen3.6-27B ships with a vision encoder supporting images, documents with OCR, and hour-scale video (up to 224K video tokens). Gemma 4 27B is also multimodal (images). Qwen adds video understanding; Gemma has stronger EU-language OCR quality.

What about Qwen3.6-35B-A3B MoE — is that better than 27B dense?

Different tradeoffs. 35B-A3B MoE is faster per-token (~3B active params) so throughput is higher on the same GPU. 27B dense beats it on coding benchmarks (SWE-bench 77.2% vs ~72%) and fits in less VRAM (16.8 vs ~21 GB Q4). For serious coding, prefer 27B. For chat speed, prefer 35B-A3B.

Which one should I run on a 24GB GPU?

Qwen3.6-27B at Q6_K (22.5 GB) for coding precision, or Q4_K_M with long context enabled. If you prefer Google's RLHF tuning or need strong European-language output, Gemma 4 27B at Q5_K_M is a solid alternative.

When did Qwen 3.6 27B release?

April 22, 2026 on Hugging Face + ModelScope under Apache-2.0. Sibling Qwen3.6-35B-A3B launched April 16, 2026. The API-only Qwen 3.6 Plus Preview launched March 30-31, 2026.

April 22, 2026Updated April 23, 2026qwen, gemma, comparison, versus, local-llm, benchmarks, qwen3.6-27b, dense

Qwen 3.6 27B vs Gemma 4 27B — Dense Head-to-Head (April 2026)

Qwen3.6-27B dense vs Gemma 4 27B dense: SWE-bench 77.2 vs 78.5 but Qwen wins Terminal-Bench 59.3 and AIME 94.1. VRAM (16.8 vs 16 GB), coding, vision, 1M context.

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.