Gemma 4 vs Qwen3.6: In-Depth Comparison of the Leading Open-Source LLMs

Last updated April 2026 — refreshed for current model/tool versions.

Gemma 4 (Google DeepMind, April 2026) and Qwen3.6 (Alibaba, April 2026) are the current generation of open-weight LLMs. Both shipped under Apache 2.0, both run usefully on a single workstation GPU, and both have eclipsed their respective Gemma 3 / Qwen 3 predecessors on coding, math, and agentic benchmarks. This guide is the head-to-head comparison: architecture, sizes, benchmarks, multimodality, licensing, and deployment footprint — refreshed with 2026 numbers.

What changed in 2026: Google replaced Gemma 3 (1B–27B, Gemma license) with Gemma 4 (E2B / E4B / 26B MoE / 31B dense, Apache 2.0). Alibaba replaced Qwen 3 with Qwen3.5 (397B-A17B MoE, Feb 2026) and Qwen3.6 (27B dense + 35B-A3B MoE + 1M-context Plus Preview, March–April 2026). The earlier claim that "Qwen 3 has no vision" was always partial — the Qwen3-VL family (2B/4B/8B/32B dense and 30B-A3B / 235B-A22B MoE) shipped between September and October 2025 and Qwen3.6-27B is natively multimodal. Both 2026 flagships now process text, images, and video out of the box.

Want the full picture? Read our continuously-updated Qwen 3.5 Complete Guide (2026) — flavors, licensing, benchmarks, and on-device usage.

TL;DR (2026)

• Pick Gemma 4 (31B dense) if you want a single-GPU, Apache-2.0 frontier model with native video + image + audio, 256K context, and 140+ languages — strong general reasoning and the broadest multimodal envelope.
• Pick Qwen3.6-27B (dense) if your priority is agentic coding, terminal/SWE-bench performance, or the new Thinking Preservation reasoning mechanism. It outperforms Qwen3.5-397B-A17B on SWE-bench Verified at ~14× fewer total parameters.
• Pick Qwen3.6-35B-A3B if VRAM is the constraint: ~3.1B active parameters per token (top-4-of-64 routing) means it runs on a used RTX 3090, while still scoring 73.4 on SWE-bench Verified and 92.7 on AIME 2026.
• Pick Qwen3.6 Plus Preview when you need a 1M-token context window and reportedly ~3× the throughput of Claude Opus 4.6 in tokens/sec.
• Skip Gemma 3 / Qwen 3 for new builds. Both families are end-of-line; their successors ship under more permissive licenses with measurably better benchmarks.

If you also need to weigh closed-weight competition for coding (Claude 4.6/4.7, GPT-5.5, DeepSeek V4), our pillar guide covers that directly: DeepSeek V4 vs Claude vs GPT-5: AI coding model comparison (2026).

Overview

Gemma 4 (Google DeepMind, released April 2, 2026)

Gemma 4 is Google's latest open-weight LLM family, the successor to Gemma 3. The headline change is licensing: Gemma 4 ships under Apache 2.0, ending the restrictive Gemma license era. The 31B dense model is currently ranked #3 on the Arena AI text leaderboard among open models; the 26B MoE sits at #6.

Key features:

• Architecture: Decoder-only transformer (dense), plus a 26B Mixture-of-Experts variant
• Parameter sizes: Effective 2B (E2B), Effective 4B (E4B), 26B MoE, 31B dense
• Multimodality: All models natively process text, images, and video at variable resolutions; E2B and E4B also accept native audio input
• Context window: 128K tokens (E2B / E4B), up to 256K tokens (26B MoE / 31B dense)
• Multilingual: 140+ languages, natively trained
• License: Apache 2.0 (commercial use, fine-tuning, redistribution permitted)
• Tooling: Native function calling, structured JSON output, system instructions for autonomous agents

Qwen3.6 / Qwen3.5 (Alibaba, February–April 2026)

Qwen3.6 is Alibaba's current flagship series. Qwen3.5 (Feb 16, 2026) introduced the 397B-A17B MoE; Qwen3.6 (March–April 2026) added the dense 27B, the 35B-A3B MoE, and the 1M-context Plus Preview. The series introduces a Thinking Preservation mechanism that retains reasoning state across multi-turn agent runs, plus a hybrid Gated DeltaNet + self-attention architecture in the 27B dense model.

Key features:

• Architecture: Dense (27B) and MoE (35B-A3B, 397B-A17B); hybrid Gated DeltaNet linear attention + self-attention in the 27B
• Parameter sizes (current): Qwen3.6-27B dense, Qwen3.6-35B-A3B (≈3.1B active), Qwen3.5-397B-A17B (≈17B active)
• Multimodality: Native image + video in Qwen3.6-27B; the Qwen3-VL line (2B / 4B / 8B / 32B dense and 30B-A3B / 235B-A22B MoE, released Sep–Oct 2025) covers vision-language workloads explicitly
• Context window: 262K native, extensible to 1M tokens (27B and 35B-A3B); 1M tokens stock on Qwen3.6 Plus Preview
• Multilingual: 100+ languages
• License: Apache 2.0
• Reasoning modes: Thinking / Non-Thinking modes carried over from Qwen 3, plus the new Thinking Preservation mechanism

Specifications Side by Side

2026 Benchmarks

All scores below are from the official model cards / launch reports for Gemma 4 (April 2026) and Qwen3.6 (April 2026). Compare cautiously: Qwen3.6 reports SWE-bench / Terminal-Bench prominently; Gemma 4's launch led with AIME 2026 and LiveCodeBench v6.

Reference points: on AIME 2026 specifically, Gemma 4 (89.2%) edges Llama 4 (88.3%) and is far ahead of DeepSeek V4 (42.5%) and the GPT family (37.5%) per Google's launch numbers. Qwen3.6-27B's 77.2% on SWE-bench Verified beats Qwen3.5-397B-A17B (76.2%) at ~14× fewer total parameters — a strong case for the dense small-flagship pattern.

How to read these scores

• Gemma 4 dominates pure math and contest reasoning at the 31B dense tier, plus it has the most balanced multimodal stack.
• Qwen3.6 dominates real-software-engineering benchmarks. SWE-bench Verified, SWE-bench Pro, and Terminal-Bench 2.0 are closer to "what actually breaks in production" than HellaSwag or GSM8K, and the Qwen3.6 dense 27B wins all three among open models at this size.
• The MoE vs dense tradeoff is now clearer. Qwen3.6-35B-A3B activates ~3.1B parameters per token, fitting on a 24 GB GPU; Gemma 4 31B dense needs more VRAM but has stronger general-purpose multimodal coverage.

Multimodal Capabilities

Note this contradicts what older Gemma 3 vs Qwen 3 comparisons (including the prior version of this article) said. Qwen 3 always had vision-capable variants — the Qwen-VL line predates Qwen3 and the Qwen3-VL series shipped through Sep–Oct 2025 with sizes from 2B to 235B-A22B. With Qwen3.6-27B, multimodality is in the base dense model rather than a separate "VL" SKU.

Deployment and Hardware Footprint

Quantization and runtimes

• Both families ship official GGUF / AWQ / GPTQ quantizations day-of-release on Hugging Face.
• Gemma 4 has first-party LM Studio, Ollama, and Vertex AI integration; the E-sizes are explicitly tuned for browser-side WebGPU.
• Qwen3.6-27B and 35B-A3B are supported in vLLM, SGLang, llama.cpp, and Ollama; the 1M-context Plus Preview is currently free via OpenRouter.

Licensing

This is the simplest comparison in the article in 2026: both Gemma 4 and Qwen3.6 are Apache 2.0. The old "Gemma license is restrictive, Qwen is permissive" tradeoff no longer applies. Either model can be used commercially, fine-tuned, and redistributed without per-MAU caps or use-case restrictions. The remaining differentiation is technical, not legal.

Reasoning, Coding, and Math

• Math contests (AIME 2026): Qwen3.6-35B-A3B (92.7%) edges Gemma 4 31B dense (89.2%). Both are well above the closed-weight competition cited in Google's launch.
• Software engineering (SWE-bench Verified, Terminal-Bench 2.0): Qwen3.6-27B is the open-weight leader at this size class.
• General coding (LiveCodeBench v6): Gemma 4 31B at 80.0% is competitive with Llama 4 (77.1%) and far ahead of DeepSeek V4 (52.0%) and GPT (44.0%) on Google's reported numbers.
• Agentic workflows: Both have native function calling and structured-output support. Qwen3.6 adds Thinking Preservation, which the team reports improves multi-turn agent stability.

Use Cases: Which to Pick

Gemma 4 best for:

• Multimodal apps spanning text, image, video, and audio
• On-device / browser deployments (E2B and E4B)
• Wide-language coverage (140+ languages, including low-resource)
• STEM and contest-style reasoning
• Teams already invested in Vertex AI / LM Studio / Ollama

Qwen3.6 best for:

• Agentic coding pipelines (SWE-bench / Terminal-Bench)
• Long-context workloads up to 1M tokens (Plus Preview)
• VRAM-constrained deployments via 35B-A3B MoE
• High-throughput cloud serving via Qwen3.5-397B-A17B
• Vision-language pipelines via the Qwen3-VL SKUs (still actively maintained)

How This Compares to Closed-Weight Models

Both Gemma 4 and Qwen3.6 are competitive with — and on specific benchmarks beat — closed-weight peers in 2026. The pillar comparison breaks the closed-weight side down in detail: DeepSeek V4 vs Claude vs GPT-5: AI coding model comparison (2026). Short version: Claude 4.6/4.7 still leads on long-horizon agentic coding, GPT-5.5 leads on tool-use latency, and DeepSeek V4 is the closed-weight price-performance leader; Qwen3.6 is the strongest open-weight equivalent, and Gemma 4 is the strongest open-weight multimodal equivalent.

FAQs

Which model is best for coding in 2026?

• Among open weights, Qwen3.6-27B (dense) wins SWE-bench Verified at 77.2%. For VRAM-tight setups, Qwen3.6-35B-A3B is the practical pick.

Can either model run on a single consumer GPU?

• Yes. Qwen3.6-35B-A3B runs on a 24 GB card (RTX 3090/4090) thanks to ~3.1B active params. Gemma 4 E4B runs on most laptops; the 31B dense needs a workstation-class card or quantization.

Does Qwen support vision now?

• Yes — and it always did. The Qwen3-VL family (2B/4B/8B/32B dense, 30B-A3B / 235B-A22B MoE) covers dedicated vision-language workloads, and Qwen3.6-27B has native multimodal support in the base dense model.

Are both Apache 2.0?

• Yes, as of April 2026. The old Gemma license restriction is gone with Gemma 4.

Which has the longer context?

• Qwen3.6 — 262K native, extensible to 1M; Plus Preview is 1M out of the box. Gemma 4 caps at 256K on the larger sizes.

What about Gemma 3 / Qwen 3 if I'm already running them?

• Both still work, but for new builds the successors are strictly better on benchmarks, more permissively licensed, and have broader multimodal support. Plan a migration.

Final Comparison Summary

Conclusion

Choose Gemma 4 when your workload is multimodal-heavy, multilingual, or needs to run on edge / mobile / browser devices, and especially if you want a single model that handles text, image, video, and audio under a permissive license.

Choose Qwen3.6 when your workload is agentic coding, long-context retrieval-augmented generation, or VRAM-constrained inference. The 27B dense or the 35B-A3B MoE will out-perform Gemma 4 on real software-engineering benchmarks while remaining Apache 2.0.

For most teams in 2026, the practical answer is "both" — Gemma 4 for product surfaces that touch users and media, Qwen3.6 for backend coding and tool-using agents.

Related on Codersera

• DeepSeek V4 vs Claude vs GPT-5: AI coding model comparison (2026) — pillar coding-model comparison
• How to Run Gemma on a Mac: A Comprehensive Guide
• Qwen3-VL-30B-A3B-Thinking: Complete Deployment Guide
• Run Qwen3-VL-30B-A3B-Thinking on macOS: Installation Guide
• Best Cloud GPUs for LLMs