Qwen3.5-27B vs Gemma 3 27B: Same Parameter Count, Completely Different Models

The number 27 billion is the only thing these two models have in common. Qwen3.5-27B and Gemma 3 27B are both dense transformer-class models with roughly the same parameter count, both target single-GPU deployment, and both claim to be open. But the benchmark gap between them is not small. It is enormous. On MMLU-Pro, Qwen scores 86.1 versus Gemma's 67.5 - an 18.6-point spread. On GPQA Diamond, Qwen's 85.5 versus Gemma's 42.4 is a 43-point gap. These are not incremental differences. They suggest fundamentally different capability tiers.

But benchmarks do not tell the whole story, and Gemma has genuine strengths that Qwen cannot match. Let us dig into the data.

Quick Comparison

Qwen3.5-27B: The Reasoning Powerhouse

Qwen3.5-27B is part of Alibaba's February 2026 Qwen 3.5 release, and it arrived with benchmark numbers that would have been flagship-tier twelve months ago. MMLU-Pro at 86.1, GPQA Diamond at 85.5, SWE-bench Verified at 72.4 - these are scores that put it ahead of many 70B+ models from previous generations. The model fixes 72% of real GitHub issues on SWE-bench, which is not an abstract metric. That translates directly into utility for agentic coding workflows.

The architecture is where things get interesting. Qwen3.5-27B uses a hybrid of Gated DeltaNet layers and standard Gated Attention layers in a 3:1 ratio. Three out of every four layers use DeltaNet - a form of linear attention that scales O(n) with sequence length - while every fourth layer uses full quadratic attention for global context aggregation. The practical result is a model that handles long sequences more efficiently than a pure transformer while still maintaining the attention quality where it matters most. The 64-layer stack uses 48 linear attention heads for the DeltaNet layers and 24 full attention heads with 4 KV heads for the global layers.

The context window is 262,144 tokens natively, extendable to over 1 million with YaRN scaling. That is double Gemma's 128K ceiling. For workloads that require processing entire codebases, long documents, or extended multi-turn conversations, this is a meaningful practical difference. The model also ships with Multi-Token Prediction (MTP) training, which improves inference throughput by predicting multiple tokens per step. Combined with the Apache 2.0 license - the most permissive option available - Qwen gives developers maximum flexibility for commercial deployment. For a broader look at how Qwen 3.5 models compare across the family, the 27B sits in a sweet spot between the 35B-A3B MoE and the 122B-A10B.

Gemma 3 27B: The Ecosystem Play

Gemma 3 27B launched in March 2025, which makes it nearly a year older than Qwen3.5-27B. That age gap matters - a year in LLM development is an eternity - but it does not invalidate Gemma's genuine strengths.

The model uses a standard transformer architecture with a 5:1 local-to-global attention ratio, meaning five layers use sliding-window local attention for every one layer of full global attention. This design prioritizes memory efficiency at long context lengths. Google trained the model on 14 trillion tokens across more than 140 languages, making it one of the most linguistically diverse open models available. If your deployment target includes underrepresented languages, Gemma's training data breadth is a real advantage.

Vision is Gemma's other differentiator. It supports native image understanding - not a bolted-on adapter, but a first-class capability. On ChartQA, DocVQA, and similar vision benchmarks, Gemma 3 27B performs competitively. For use cases like document analysis, chart reading, or visual question answering where the input is an image paired with a question, Gemma provides a capable single-model solution. Qwen3.5-27B also has native multimodal support (including video), but Gemma's vision capabilities arrived earlier and have had more community testing.

The license situation requires a careful read. Gemma ships under Google's "Gemma Terms of Use," which permits commercial deployment but includes provisions that Apache 2.0 does not. Notably, Google reserves the right to restrict usage that violates their prohibited use policy. For most standard commercial applications, this is unlikely to be a problem - but if you are building in a sensitive domain or need the absolute simplest licensing terms, the distinction matters. See our guide to open-source vs proprietary AI for more context on how different licenses affect deployment decisions.

The Google Cloud ecosystem integration is Gemma's strongest operational advantage. The model is optimized for TPU deployment, available directly through Vertex AI, and integrates smoothly with Google's broader AI tooling. If your infrastructure is already on GCP, Gemma slots in with minimal friction. Deploying Qwen on Google hardware is possible but not the path of least resistance.

Detailed Benchmark Comparison

The numbers are blunt. On the benchmarks where both models have reported scores, Qwen leads by double-digit margins on nearly every reasoning and coding metric. The GPQA Diamond gap of 43 points is the most striking - this benchmark tests graduate-level scientific reasoning, and a 43-point difference represents a fundamentally different capability tier. The LiveCodeBench gap of 51 points tells a similar story for competitive programming tasks.

There are important caveats. First, these models are nearly a year apart in release date. Comparing a February 2026 model to a March 2025 model is not an apples-to-apples test of architectural quality - it is also a test of training data, post-training techniques, and the state of the art at each model's release time. Second, these benchmarks are self-reported by their respective creators. Independent verification from sources like the Open Source LLM Leaderboard and Coding Benchmarks Leaderboard should be consulted as community-reproduced scores become available.

Gemma shows strength on HumanEval (87.8) and GSM8K (95.9), but these are relatively saturated benchmarks where most modern models score highly. The harder benchmarks - GPQA Diamond, LiveCodeBench, SWE-bench - are where the meaningful separation appears.

Pricing and Deployment

Both models are 27B dense, so self-hosting requirements are nearly identical. You need roughly 54GB of VRAM for BF16 inference, or around 16GB with aggressive 4-bit quantization (fits on an RTX 4090 or equivalent). The hardware cost is the same - the difference is what you get for that hardware investment.

On managed API pricing, Gemma has a slight edge through Vertex AI's $0.04/M input pricing, though Qwen's hosted options through Alibaba Cloud and third-party providers are competitive. For self-hosting, both models can be served through vLLM and SGLang. Gemma has the advantage of TPU-native support and broader framework maturity given its year-long head start.

The cost-per-useful-output calculation favors Qwen for any task where accuracy matters. If Qwen produces a correct answer 85% of the time on a reasoning task where Gemma manages 42%, the per-token savings on Gemma are irrelevant because you need more attempts, more human review, or more fallback logic to compensate.

Pros and Cons

Qwen3.5-27B

Pros:

• MMLU-Pro 86.1, GPQA Diamond 85.5 - best-in-class reasoning at 27B parameters
• SWE-bench Verified 72.4 - viable for automated code repair
• 262K context window (1M+ extended) - double Gemma's capacity
• Gated DeltaNet hybrid architecture delivers efficient long-context processing
• Apache 2.0 license - maximum commercial flexibility
• Native multimodal including video understanding
• Part of broader Qwen 3.5 ecosystem with MoE variants

Cons:

• Gated DeltaNet architecture is newer - framework support still maturing
• February 2026 release means less community testing and fine-tuning ecosystem
• Self-reported benchmarks pending independent verification
• Thinking mode enabled by default may increase output token usage
• Less Google Cloud integration compared to Gemma

Gemma 3 27B

Pros:

• Native vision capabilities with strong DocVQA and ChartQA scores
• TPU-optimized - best-in-class deployment on Google Cloud
• 140+ language support with deep multilingual training
• Mature ecosystem - one year of community testing, fine-tuning, and deployment experience
• Strong GSM8K (95.9) and HumanEval (87.8) performance
• Low API cost through Vertex AI ($0.04/M input)

Cons:

• MMLU-Pro 67.5 trails Qwen by 18.6 points
• GPQA Diamond 42.4 is a 43-point gap - fundamentally lower reasoning capability
• 128K context window is half of Qwen's 262K
• Gemma Terms of Use includes provisions Apache 2.0 does not (Google can restrict usage)
• No SWE-bench or LiveCodeBench scores reported
• March 2025 release - nearly a year behind on training data and techniques

Verdict

This comparison has a clear winner on raw capability - but capability is not the only variable that matters in a deployment decision.

Choose Qwen3.5-27B if you are optimizing for reasoning, coding, or any task where benchmark accuracy correlates with real-world utility. The GPQA gap of 43 points and the SWE-bench score of 72.4 are not marketing noise - they represent a genuine generational improvement. If you are building coding agents, research assistants, or any system where the model needs to reason deeply, Qwen is the obvious choice at this parameter count. Check the Coding Benchmarks Leaderboard to see how it stacks up against the full field.

Choose Gemma 3 27B if your deployment is on Google Cloud with TPU infrastructure, your use case is primarily vision-oriented (document analysis, chart reading), or you need the stability of a model with a year of production deployment behind it. Gemma's ecosystem maturity, multilingual breadth, and Google Cloud integration are real advantages for teams already invested in that stack.

Choose either if you need a 27B dense model for general conversational AI where neither extreme reasoning nor vision is the primary requirement. Both models handle standard chat, summarization, and light coding tasks competently. But if you are choosing blind and have no ecosystem constraints, the benchmark data makes the decision straightforward: Qwen3.5-27B is the stronger model by a significant margin, and Apache 2.0 is the simpler license. The year of architectural progress between these models is clearly visible in the numbers.

For more context on how these models fit into the broader open-weight landscape, see the Open Source LLM Leaderboard and the Phi-4 comparison for a different size-class tradeoff.

Sources:

• Qwen3.5-27B Model Card (HuggingFace)
• Qwen3.5: Towards Native Multimodal Agents (Blog)
• Gemma 3 Model Card (Google AI)
• Gemma 3 Technical Report (arXiv)
• Gemma 3 27B Benchmarks and Pricing (Artificial Analysis)
• Gemma Terms of Use
• Qwen3.5 Features, Access, and Benchmarks (DataCamp)