Gemma-4 31B vs Qwen3.5 27B: Local Model Comparison

Two of the best open-source models in the same weight class, on the same hardware, one GPU, one winner. This is a head-to-head comparison between Google DeepMind’s Gemma-4 31B and Alibaba’s Qwen3.5 27B. Both are dense, multimodal, Apache-2.0 models running locally on a single GPU, tested on coding, reasoning, vision, and multilingual tasks with identical prompts and identical parameters.

No cherry-picking, no favoritism. On paper it is too close to call, which is exactly why I ran the tests myself. I run a lot of open-source Apache-2 models for clients and I need to decide what to use going forward, Gemma-4 or Qwen3.5.

For a full written breakdown of this matchup, see this side-by-side comparison too: Gemma 4 vs Qwen3.5 analysis.

Category	Gemma-4 31B	Qwen3.5 27B
Type	Dense, multimodal	Dense, multimodal
License	Apache-2.0	Apache-2.0
Parameters	31B	27B
Hardware used	1x Nvidia H100 80 GB	1x Nvidia H100 80 GB
Serving	vLLM, one model at a time	vLLM, one model at a time
VRAM usage	Under 80 GB with KV cache	Less than Gemma-4 with similar parameters
Context setup	Same context window for both	Same context window for both
Tool use	Enabled	Enabled
Benchmarks on paper	Edge on Codeforces ELO and multilingual	Leads on some math and GPQA
Coding test result	Produced a working one-shot HTML game, later runs inconsistent	Produced a much longer file but no animation in one shot
Multilingual test	Completed all 78 languages, including rare ones	Cut off mid-sentence on some, missed a few languages
Vision test	Accurate, organized by row, minor transcription errors	Accurate, organized by topic, better hard-equation transcription
Verdict by task	Coding winner, Multilingual winner	Vision winner

Gemma-4 31B vs Qwen3.5 27B: Local Model Comparison

Test setup and environment

• Ubuntu, single Nvidia H100 80 GB.
• Both models served locally via vLLM, one at a time, with the same context window and identical prompts.
• Tool use was enabled, and KV cache was set.

If you also compare premium assistants for long-context work, see this focused look at model families: Claude Opus versions compared.

Coding test: complex simulation in a single HTML file

Prompt summary

• Architect a complete game from scratch in a single HTML file, no external libraries.
• An ant colony defends its nest against an invading segmented snake.
• Multiple AI agents, physics, canvas rendering, game state, and win conditions.

Observations

• Gemma produced index.html for the ant colony. It ran with visuals and behavior, but on later reloads the snake stalled and did not progress.
• Qwen produced a much longer HTML file that read feature-rich on inspection, but in a one-shot run it showed no animation on load and required reloads without coming alive.
• Functional beats feature-complete but broken. Gemma wins the coding round.

For more code-focused model talk, you might also like this programming-oriented comparison: code-generation models overview.

Multilingual test: fashion show opening announcement

English source line

• “Ladies and gentlemen, welcome to the greatest runway on earth, where every stride tells a story, every fabric holds a culture. And tonight, beauty speaks every language.”

Task

• Translate the announcement into a large list of languages, including rare and regional ones.
• Output must be structured and culturally sensitive, within the specified context limit.

Observations

• Gemma completed all 78 languages, including the rare and obscure ones, and kept them present and correct.
• Qwen got cut off mid-sentence on Nepali, skipped Sinhala and Khmer, and some Scandinavian outputs were not good enough. It also struggled on Afrikaans and a few others in this one-shot pass.
• Both produced solid quality where translations were complete. Gemma wins the multilingual round for completeness and coverage.

Vision and reasoning test: handwritten physics equations

Prompt summary

• Transcribe the handwritten equations exactly as written.
• Identify the governing physical laws and explain them.
• If solvable, show step-by-step reasoning. Thorough and precise outputs expected.

Observations

• Both models identified all 30 equations correctly and explained them accurately.
• Qwen organized by topic like special relativity, dynamics, wave optics, nuclear physics, and more, with cleaner sectioning and stronger domain context.
• Qwen correctly identified equation 12 as the diffraction grating equation and equation 27 as the Duane-Hunt law. Gemma made a small error on equation 14 with an approximate transcription and had a slightly off denominator on Planck’s law. Qwen wins the vision round.

If you are exploring other strong open-source or hybrid lineups, this multi-model face-off is helpful: model families contrasted.

Features Breakdown

Gemma-4 31B

Model traits

• Dense, multimodal, Apache-2.0, 31B parameters.
• Strong multilingual capability and competitive coding behavior in a single-shot run.
• VRAM use stayed under 80 GB on H100 with KV cache enabled.

On-paper benchmarks

• Within a percentage point of Qwen on MMLU, GPQA Diamond, and LiveCodeBench.
• Edge on Codeforces ELO and multilingual benchmarks.

Qwen3.5 27B

Model traits

• Dense, multimodal, Apache-2.0, 27B parameters.
• Slightly lower VRAM footprint than Gemma at similar settings.
• Notable depth and organization in vision and physics reasoning.

On-paper benchmarks

• Very close to Gemma across general benchmarks.
• Leads on some math-style evaluations and GPQA in reported numbers.

For another angle on open models vs closed assistants, see this benchmark-driven write-up: DeepSeek vs Claude comparison.

Pros and Cons

Gemma-4 31B

Pros

• Produced a functional one-shot complex HTML simulation without external libraries.
• Completed 78-language translation set, including rare languages, within the context limit.
• Strong multilingual and code-oriented signals backed by competitive benchmarks.

Cons

• Inconsistent behavior on repeated runs of the HTML simulation.
• Minor transcription issues in a few equations during the vision test.
• Slightly higher VRAM use compared to Qwen under similar parameters.

Qwen3.5 27B

Pros

• Excellent organization by topic and depth in the vision reasoning test.
• Accurate handling of harder equations like diffraction grating and Duane-Hunt.
• Slightly lighter VRAM footprint while maintaining strong accuracy.

Cons

• One-shot HTML simulation did not animate on load, despite a much longer code file.
• Multilingual run cut off in some languages and missed a few entirely in this pass.
• Some translation quality gaps noted in Scandinavian and Afrikaans outputs.

Use Cases and Scenarios

Where Gemma-4 31B excels

• Rapid prototyping of interactive front-end simulations where a working first run matters.
• Large multilingual copy generation and batch translation tasks with broad language coverage.
• General-purpose local assistant work on a single GPU with focus on code plus text.

Where Qwen3.5 27B excels

• Technical reading, interpretation, and tutoring on math and physics-heavy content.
• Tasks needing structured, topic-wise organization and thorough domain context.
• Local deployments that want slightly lower VRAM use while maintaining reasoning depth.

If you want a written take focused only on this matchup, here is another concise side-by-side: in-depth Gemma vs Qwen notes.

Final Conclusion

Gemma-4 31B wins coding and multilingual in this one-shot local test, primarily due to a functional HTML game and complete 78-language coverage. Qwen3.5 27B wins vision and reasoning with cleaner organization, accurate handling of hard physics equations, and slightly better transcription fidelity.

Choose Gemma-4 31B if your priority is multilingual coverage and practical code outputs that run on first pass. Choose Qwen3.5 27B if your priority is technical reasoning, physics-math reading, and structured explanations with a lighter VRAM footprint. If you are surveying more comparisons beyond these two, see this broader model overview as well: Claude variants head-to-head.