Head to head

Gemma 4 26B A4B vs Qwen3.6 27B

Qwen3.6 27B scores higher on 5 of the 5 benchmarks both models publish. The widest gap is on Humanity's Last Exam, where Qwen3.6 27B leads by 15.3 points.

Gemma 4 26B A4BGoogle · OpenLLM Buddy catalog72.9compositeQwen3.6 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 26B A4B or Qwen3.6 27B?

Pick Gemma 4 26B A4B if…

  • You need fast responses: about 113 tokens per second through APIs, against 57.
  • Cost matters: $0.600 per million output tokens against $2.70.
  • You want it running in minutes on a dedicated GPU through OpenLLM Buddy.

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 it running in minutes on a dedicated GPU through OpenLLM Buddy.

Hardware and cost to run

Gemma 4 26B A4B

At 4-bit it needs about 17 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 58 GB (1× H100 (80 GB)).

On OpenLLM Buddy: Gemma 4 26B A4B, with an OpenAI-compatible API and no per-token fees. Prices start at $13 USD for 11 Hours.

Deploy Gemma 4 26B A4B
Full GPU memory table for Gemma 4 26B A4B

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 26B A4BQwen3.6 27B
Reasoning63.280.9
Coding82.577.3
Agentic56.6—
Knowledge89.399.3

Specs side by side

Gemma 4 26B A4BQwen3.6 27B
LabGoogleAlibaba Qwen
WeightsOpenOpen
Released2026-04-022026-04
Parameters25.2B total, 3.8B active (MoE)27B dense
Context256K262K (up to 1M)
LicenseApache-2.0Apache-2.0
4-bit memory17 GB · RTX 4090 (24 GB)19 GB · RTX 4090 (24 GB)
API price$0.150 in / $0.600 out per 1M tokens$0.450 in / $2.70 out per 1M tokens
API speed113 tok/s57 tok/s
One-GPU speed (est.)≈ 133–160 tok/s on RTX 4090 (4-bit)≈ 28–39 tok/s on RTX 4090 (4-bit)

Related comparisons

Sources

ModelTestScoreSettingSource
Gemma 4 26B A4BGPQA Diamond82.3—Gemma 4 31B IT model card (Hugging Face), 26B A4B column
Qwen3.6 27BGPQA Diamond87.8—Qwen3.6-27B model card (Hugging Face)
Gemma 4 26B A4BHumanity's Last Exam8.7no toolsGemma 4 31B IT model card (Hugging Face), 26B A4B column
Qwen3.6 27BHumanity's Last Exam24.0—Qwen3.6-27B model card (Hugging Face)
Gemma 4 26B A4BAIME88.3AIME 2026, no toolsGemma 4 31B IT model card (Hugging Face), 26B A4B column
Qwen3.6 27BAIME94.1AIME 2026Qwen3.6-27B model card (Hugging Face)
Gemma 4 26B A4BLiveCodeBench77.1LiveCodeBench v6Gemma 4 31B IT model card (Hugging Face), 26B A4B column
Qwen3.6 27BLiveCodeBench83.9LiveCodeBench v6Qwen3.6-27B model card (Hugging Face)
Gemma 4 26B A4BMMLU-Pro82.6—Gemma 4 31B IT model card (Hugging Face), 26B A4B column
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