Gemma 4 26B A4B IT vs Qwen3.6 27B
Qwen3.6 27B leads llmrun Score 111.4 to 105.4 (ranges overlap); both fit 24 GB at Q4_K_M; Gemma 4 26B A4B IT is 4.5× faster on RTX 4090 (~171 vs ~38 tok/s).
Model A · Google
Gemma 4 26B A4B IT25.8B · 4.4B active · 256K context · Apache 2.0 · Mar 2026
Model B · Alibaba
Qwen3.6 27B27.8B · 256K context · Apache 2.0 · Apr 2026
Choose Gemma 4 26B A4B IT if you want…
4.5× faster on RTX 4090
~171 vs ~38 tok/s
Choose Qwen3.6 27B if you want…
Higher llmrun Score
111.4 vs 105.4, ranges overlap
Stronger at science
111.1 vs 102.2
Ahead on the benchmarks
6 of 7 shared boards
On your hardware
Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.
8 GB
RTX 4060 · 8 GB tier
Gemma 4 26B A4B IT
Doesn't fit
smallest quant needs 8.1 GB
Qwen3.6 27B
Doesn't fit
smallest quant needs 8.8 GB
12 GB
RTX 3060 12GB · 12 GB tier
Gemma 4 26B A4B IT
IQ3_S · ~99 tok/s
12 GB · up to 16K
Qwen3.6 27B
IQ3_XXS · ~20 tok/s
12 GB · up to 16K
16 GB
RTX 5060 Ti 16GB · 16 GB tier
Gemma 4 26B A4B IT
Q4_1 · ~95 tok/s
15.5 GB · up to 16Kbetter quant
Qwen3.6 27B
Q3_K_L · ~19 tok/s
15.4 GB · up to 16K
24 GBreference
RTX 4090 · 24 GB tier
Gemma 4 26B A4B IT
Q4_K_M · ~171 tok/s
16.5 GB · up to 128Kfaster
Qwen3.6 27B
Q4_K_M · ~38 tok/s
17.9 GB · up to 64K
32 GB
RTX 5090 · 32 GB tier
Gemma 4 26B A4B IT
Q4_K_M · ~250 tok/s
16.5 GB · up to 256Kfaster
Qwen3.6 27B
Q4_K_M · ~67 tok/s
17.9 GB · up to 256K
48 GB
RTX A6000 · 48 GB tier
Gemma 4 26B A4B IT
Q4_K_M · ~140 tok/s
16.5 GB · up to 256Kfaster
Qwen3.6 27B
Q4_K_M · ~29 tok/s
17.9 GB · up to 256K
Mac 32 GB
Mac Mini M4 · Macs
Gemma 4 26B A4B IT
Q4_K_M · ~26 tok/s
16.5 GB · up to 256Kfaster
Qwen3.6 27B
Q4_K_M · ~5 tok/s
17.9 GB · up to 128K
Mac 64 GB
Mac Studio M4 Max · Macs
Gemma 4 26B A4B IT
Q4_K_M · ~76 tok/s
16.5 GB · up to 256Kfaster
Qwen3.6 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256K
Mac 128 GB
Mac Studio M4 Max · Macs
Gemma 4 26B A4B IT
Q4_K_M · ~76 tok/s
16.5 GB · up to 256Kfaster
Qwen3.6 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256K
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
Gemma 4 26B A4B IT
Qwen3.6 27B
Coding
Gemma 4 26B A4B IT
Qwen3.6 27B
Math
Gemma 4 26B A4B IT
no data
Qwen3.6 27B
Science
Gemma 4 26B A4B IT
Qwen3.6 27B
Reasoning
Gemma 4 26B A4B IT
Qwen3.6 27B
Gemma 4 26B A4B IT · #37 of 92 open models · #6 of 34 that fit 24 GB
Qwen3.6 27B · #20 of 92 open models · #2 of 34 that fit 24 GB
Benchmarks
7 shared boards: Gemma 4 26B A4B IT leads 1, Qwen3.6 27B leads 6 · Benchmarks imported Oct 2026
Gemma 4 26B A4B IT
73.2%#29/97 open
IndependentEpoch AI · Apr 2026
Qwen3.6 27B
85.9%#18/97 open
IndependentEpoch AI · Apr 2026
Gemma 4 26B A4B IT
82.2%#23/85 open
IndependentEpoch AI · Apr 2026
Qwen3.6 27B
91.1%#11/85 open
IndependentEpoch AI · Apr 2026
Gemma 4 26B A4B IT
40.1%#30/65 open
Lab-reportedEpoch AI · Apr 2026
Qwen3.6 27B
37.3%#35/65 open
Lab-reportedEpoch AI · Apr 2026
Gemma 4 26B A4B IT
6.0%#27/67 open
IndependentEpoch AI · Apr 2026
Qwen3.6 27B
22.0%#6/67 open
IndependentEpoch AI · Apr 2026
Gemma 4 26B A4B IT
0.0%#50/65 open
Lab-reportedEpoch AI · Apr 2026
Qwen3.6 27B
0.9%#36/65 open
Lab-reportedEpoch AI · Apr 2026
Gemma 4 26B A4B IT
74.9%#29/68 open
Lab-reportedEpoch AI · Apr 2026
Qwen3.6 27B
78.1%#26/68 open
Lab-reportedEpoch AI · Apr 2026
Gemma 4 26B A4B IT
29.7%#20/54 open
Lab-reportedEpoch AI · Apr 2026
Qwen3.6 27B
34.5%#15/54 open
Lab-reportedEpoch AI · Apr 2026
9 more boards (other shared and single-model boards)
Only Gemma 4 26B A4B IT (4)
- LMArena Text1437.6
- LMArena WebDev1359.0
- WeirdML35.2%
- ALE-Bench927.2
Only Qwen3.6 27B (5)
- LiveBench Coding71.8
- LiveBench Reasoning70.3
- FrontierMath35.1%
- LiveBench Math79.9
- Mystery Game Puzzles7.0%
VRAM by quantization
Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.
Q2_K
Gemma 4 26B A4B IT
11.6 GB11.0 GB file
32K 12.5 GB · 128K 15.8 GB
Qwen3.6 27B
12.6 GB12.1 GB file
32K 13.9 GB · 128K 19.3 GB
Q3_K_S
Gemma 4 26B A4B IT
11.9 GB12.6 GB file
32K 12.8 GB · 128K 16.2 GB
Qwen3.6 27B
12.9 GB12.4 GB file
32K 14.2 GB · 128K 19.6 GB
Q3_K_M
Gemma 4 26B A4B IT
13.2 GB12.7 GB file
32K 14.1 GB · 128K 17.5 GB
Qwen3.6 27B
14.3 GB13.6 GB file
32K 15.6 GB · 128K 21.0 GB
Q4_0
Gemma 4 26B A4B IT
13.6 GB~12.9 GB file
32K 14.5 GB · 128K 17.8 GB
Qwen3.6 27B
14.6 GB15.8 GB file
32K 16.0 GB · 128K 21.4 GB
Q4_K_M
Gemma 4 26B A4B IT
16.1 GB16.9 GB file
32K 17.0 GB · 128K 20.4 GB
Qwen3.6 27B
17.4 GB16.8 GB file
32K 18.8 GB · 128K 24.1 GB
Q5_K_M
Gemma 4 26B A4B IT
19.0 GB21.2 GB file
32K 19.9 GB · 128K 23.3 GB
Qwen3.6 27B
20.5 GB19.5 GB file
32K 21.9 GB · 128K 27.3 GB
Q6_K
Gemma 4 26B A4B IT
21.9 GB23.2 GB file
32K 22.8 GB · 128K 26.2 GB
Qwen3.6 27B
23.7 GB22.5 GB file
32K 25.0 GB · 128K 30.4 GB
Q8_0
Gemma 4 26B A4B IT
26.5 GB~25.8 GB file
32K 27.4 GB · 128K 30.7 GB
Qwen3.6 27B
28.5 GB28.6 GB file
32K 29.9 GB · 128K 35.2 GB
19 more quants
IQ2_XXS
Gemma 4 26B A4B IT
7.7 GB9.9 GB file
32K 8.6 GB · 128K 12.0 GB
Qwen3.6 27B
8.4 GB9.4 GB file
32K 9.7 GB · 128K 15.1 GB
IQ2_XS
Gemma 4 26B A4B IT
8.4 GB10.1 GB file
32K 9.3 GB · 128K 12.6 GB
Qwen3.6 27B
9.1 GB10.2 GB file
32K 10.4 GB · 128K 15.8 GB
IQ2_S
Gemma 4 26B A4B IT
8.7 GB10.2 GB file
32K 9.6 GB · 128K 12.9 GB
Qwen3.6 27B
9.4 GB10.5 GB file
32K 10.8 GB · 128K 16.1 GB
IQ2_M
Gemma 4 26B A4B IT
9.4 GB10.0 GB file
32K 10.3 GB · 128K 13.6 GB
Qwen3.6 27B
10.1 GB10.8 GB file
32K 11.5 GB · 128K 16.8 GB
IQ3_XXS
Gemma 4 26B A4B IT
10.7 GB11.4 GB file
32K 11.6 GB · 128K 14.9 GB
Qwen3.6 27B
11.5 GB12.0 GB file
32K 12.9 GB · 128K 18.2 GB
Q2_K_S
Gemma 4 26B A4B IT
11.0 GB10.6 GB file
32K 11.9 GB · 128K 15.2 GB
Qwen3.6 27B
not listed
IQ3_XS
Gemma 4 26B A4B IT
11.3 GB12.4 GB file
32K 12.2 GB · 128K 15.5 GB
Qwen3.6 27B
12.2 GB13.5 GB file
32K 13.6 GB · 128K 18.9 GB
IQ3_S
Gemma 4 26B A4B IT
11.6 GB11.3 GB file
32K 12.5 GB · 128K 15.8 GB
Qwen3.6 27B
not listed
IQ3_M
Gemma 4 26B A4B IT
12.3 GB13.3 GB file
32K 13.2 GB · 128K 16.5 GB
Qwen3.6 27B
13.3 GB14.1 GB file
32K 14.6 GB · 128K 20.0 GB
Q3_K_L
Gemma 4 26B A4B IT
13.9 GB13.2 GB file
32K 14.8 GB · 128K 18.1 GB
Qwen3.6 27B
15.0 GB15.5 GB file
32K 16.3 GB · 128K 21.7 GB
IQ4_XS
Gemma 4 26B A4B IT
14.5 GB13.6 GB file
32K 15.4 GB · 128K 18.7 GB
Qwen3.6 27B
15.7 GB15.4 GB file
32K 17.0 GB · 128K 22.4 GB
IQ4_NL
Gemma 4 26B A4B IT
15.2 GB13.6 GB file
32K 16.1 GB · 128K 19.4 GB
Qwen3.6 27B
16.4 GB16.1 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_1
Gemma 4 26B A4B IT
15.2 GB16.1 GB file
32K 16.1 GB · 128K 19.4 GB
Qwen3.6 27B
16.4 GB17.3 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_S
Gemma 4 26B A4B IT
15.2 GB16.5 GB file
32K 16.1 GB · 128K 19.4 GB
Qwen3.6 27B
16.4 GB15.9 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_L
Gemma 4 26B A4B IT
16.5 GB17.2 GB file
32K 17.4 GB · 128K 20.7 GB
Qwen3.6 27B
17.8 GB18.9 GB file
32K 19.1 GB · 128K 24.5 GB
Q5_0
Gemma 4 26B A4B IT
16.8 GB17.5 GB file
32K 17.7 GB · 128K 21.0 GB
Qwen3.6 27B
not listed
Q5_K_S
Gemma 4 26B A4B IT
18.4 GB18.9 GB file
32K 19.3 GB · 128K 22.6 GB
Qwen3.6 27B
19.9 GB19.0 GB file
32K 21.2 GB · 128K 26.6 GB
Q5_K_L
Gemma 4 26B A4B IT
19.4 GB19.5 GB file
32K 20.3 GB · 128K 23.6 GB
Qwen3.6 27B
20.9 GB21.7 GB file
32K 22.2 GB · 128K 27.6 GB
BF16
Gemma 4 26B A4B IT
52.3 GB~51.6 GB file
32K 53.2 GB · 128K 56.5 GB
Qwen3.6 27B
56.3 GB53.8 GB file
32K 57.7 GB · 128K 63.0 GB
Gemma 4 26B A4B IT
Ollama · Q4_K_M
ollama run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:Q4_K_MQwen3.6 27B
Ollama · Q4_K_M
ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q4_K_M| Spec | Gemma 4 26B A4B IT | Qwen3.6 27B |
|---|---|---|
| Publisher | Alibaba | |
| Parameters | 25.8B | 27.8B |
| Active parameters | 4.4B per token | — |
| Architecture | Gemma4ForConditionalGeneration | Qwen3_5ForConditionalGeneration |
| Context | 256K tokens | 256K tokens |
| Modalities | Vision, Function-calling | Vision, Function-calling |
| License | Apache 2.0 | Apache 2.0 |
| Released | Mar 2026 | Apr 2026 |
Frequently Asked Questions
Which is better, Gemma 4 26B A4B IT or Qwen3.6 27B?
Qwen3.6 27B leads llmrun Score 111.4 to 105.4 (ranges overlap); both fit 24 GB at Q4_K_M; Gemma 4 26B A4B IT is 4.5× faster on RTX 4090 (~171 vs ~38 tok/s). Choose Gemma 4 26B A4B IT for 4.5× faster on RTX 4090; choose Qwen3.6 27B for higher llmrun Score and stronger at science.
Which needs less VRAM, Gemma 4 26B A4B IT or Qwen3.6 27B?
At Q4_K_M, Gemma 4 26B A4B IT requires 16.1 GB while Qwen3.6 27B needs 17.4 GB, making Gemma 4 26B A4B IT the easier model to run on your local setup.
Which is faster on RTX 4090?
Estimated decode speed on RTX 4090 is about 171 tok/s for Gemma 4 26B A4B IT (Q4_K_M) and 38 tok/s for Qwen3.6 27B (Q4_K_M), so Gemma 4 26B A4B IT is faster. Estimates, ±20%.
Can I run Gemma 4 26B A4B IT or Qwen3.6 27B on a 16 GB GPU?
Gemma 4 26B A4B IT fits at Q4_1; Qwen3.6 27B fits at Q3_K_L.
Which has a longer context window, Gemma 4 26B A4B IT or Qwen3.6 27B?
Gemma 4 26B A4B IT supports 262,144 tokens and Qwen3.6 27B supports 262,144 tokens.