Head to head

Gemma 4 31B vs Qwen3.6 27B

Qwen3.6 27B scores higher on 5 of the 5 benchmarks both models publish. The widest gap is on AIME, where Qwen3.6 27B leads by 4.9 points.

Gemma 4 31BGoogle · OpenLLM Buddy catalog79.2compositeQwen3.6 27BAlibaba Qwen · OpenLLM Buddy catalog85.8composite

Shared benchmarks

Raw published scores; the higher score on each row is highlighted. LMArena Elo is a human-preference vote and is not counted as a win.

Should you pick Gemma 4 31B or Qwen3.6 27B?

Pick Gemma 4 31B if…

  • Cost matters: $0.340 per million output tokens against $2.70.
  • You want it running in minutes on a dedicated GPU through OpenLLM Buddy, in an uncensored build.

Pick Qwen3.6 27B if…

  • You care most about GPQA Diamond, Humanity's Last Exam, AIME, where it scores at least 2 points higher.
  • You want the lighter model to host: about 19 GB at 4-bit against 21 GB.
  • You need fast responses: about 57 tokens per second through APIs, against 35.
  • You want it running in minutes on a dedicated GPU through OpenLLM Buddy.

Hardware and cost to run

Gemma 4 31B

At 4-bit it needs about 21 GB, which fits a single RTX 4090, so it can run on a home workstation with llama.cpp, Ollama, or vLLM. Full BF16 precision needs about 71 GB (1× H100 (80 GB)).

On OpenLLM Buddy: gemma-4-31B-it-uncensored runs on RTX 4090 24GB, with an OpenAI-compatible API and no per-token fees. Prices start at $13 USD for 11 hours.

Deploy Gemma 4 31B
Full GPU memory table for Gemma 4 31B

Qwen3.6 27B

At 4-bit it needs about 19 GB, which fits a single RTX 4090, so it can run on a home workstation with llama.cpp, Ollama, or vLLM. Full BF16 precision needs about 62 GB (1× H100 (80 GB)).

On OpenLLM Buddy: Qwen3.6 27B A4B, with an OpenAI-compatible API and no per-token fees. Prices start at $17 USD for 11 Hours.

Deploy Qwen3.6 27B
Full GPU memory table for Qwen3.6 27B

Category profile

Category index uses every test each model reports, not only the shared ones.

ReasoningCodingAgenticKnowledge
Gemma 4 31BQwen3.6 27B
Reasoning69.880.9
Coding85.677.3
Agentic68.9—
Knowledge92.599.3

Specs side by side

Gemma 4 31BQwen3.6 27B
LabGoogleAlibaba Qwen
WeightsOpenOpen
Released2026-04-022026-04
Parameters30.7B dense27B dense
Context256K262K (up to 1M)
LicenseApache-2.0Apache-2.0
4-bit memory21 GB · RTX 4090 (24 GB)19 GB · RTX 4090 (24 GB)
API price$0.100 in / $0.340 out per 1M tokens$0.450 in / $2.70 out per 1M tokens
API speed35 tok/s57 tok/s
One-GPU speed (est.)≈ 25–34 tok/s on RTX 4090 (4-bit)≈ 28–39 tok/s on RTX 4090 (4-bit)

Related comparisons

Sources

ModelTestScoreSettingSource
Gemma 4 31BGPQA Diamond84.3—Gemma 4 31B IT model card (Hugging Face)
Qwen3.6 27BGPQA Diamond87.8—Qwen3.6-27B model card (Hugging Face)
Gemma 4 31BHumanity's Last Exam19.5no toolsGemma 4 31B IT model card (Hugging Face)
Qwen3.6 27BHumanity's Last Exam24.0—Qwen3.6-27B model card (Hugging Face)
Gemma 4 31BAIME89.2AIME 2026, no toolsGemma 4 31B IT model card (Hugging Face)
Qwen3.6 27BAIME94.1AIME 2026Qwen3.6-27B model card (Hugging Face)
Gemma 4 31BLiveCodeBench80.0LiveCodeBench v6Gemma 4 31B IT model card (Hugging Face)
Qwen3.6 27BLiveCodeBench83.9LiveCodeBench v6Qwen3.6-27B model card (Hugging Face)
Gemma 4 31BMMLU-Pro85.2—Gemma 4 31B IT model card (Hugging Face)
Qwen3.6 27BMMLU-Pro86.2—Qwen3.6-27B model card (Hugging Face)

Scores are published results from model cards, lab reports, and public leaderboards, each linked to its source. Settings such as thinking mode or test version differ between sources and are shown next to each score. OpenLLM Buddy catalog models are uncensored fine-tunes; their scores are those of the official base model. Data collected 2026-10-30. How the index works