Skip to page content
SAVRN
Search Plan a project

SAVRN Model Hub · Comparisons

gemma-4-26B-A4B-it vs Qwen3.6-35B-A3B-NVFP4

Gemma-4-26B-A4B-it has 25.8B parameters and Qwen3.6-35B-A3B-NVFP4 has 24.6B parameters; both are released under Apache License 2.0; at 16-bit, gemma-4-26B-A4B-it needs about 61.9 GB (1x MI300X from $1.85 an hour) and Qwen3.6-35B-A3B-NVFP4 about 59.1 GB (1x MI300X from $1.85 an hour).

Published metadata for 2 models, each read from its own repository.
Field gemma-4-26B-A4B-it
google/gemma-4-26B-A4B-it
Qwen3.6-35B-A3B-NVFP4
unsloth/Qwen3.6-35B-A3B-NVFP4
Publisher Google Unsloth AI
Task Image and text to text Image and text to text
Modality Image and text Image and text
Parameters, as reported 25.8B parameters 24.6B parameters
Architecture Gemma4ForConditionalGeneration Qwen3_5MoeForConditionalGeneration
Library transformers transformers
Context length 262,144 tokens 262,144 tokens
Repository size 51.6 GB 26.5 GB
Artifact formats safetensors safetensors
License apache-2.0 apache-2.0
Access Open weights, no gate Open weights, no gate
Memory at 16-bit (weights and margin) 61.9 GB 59.1 GB
Cheapest GPUs at 16-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Memory at 4-bit (weights and margin) 15.5 GB 14.8 GB
Cheapest GPUs at 4-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Revision viewed 4d7ae4984b7d 739af1e7aac3
Downloads reported by the hub 11.2M 1.1M
Last observed 2026-10-16 2026-09-30

An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.

Other Reported Results

These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.

gemma-4-26B-A4B-it

BenchmarkConditionsResultReported byRevisionDate
Idavidrein/gpqa Task diamondMetric diamondComparison conditions not established 82.3 Model Card
Reported by a third party
Evaluated revision not stated 2026-04-02
MMMU/MMMU_Pro Task mmmu_pro_visionMetric mmmu_pro_visionComparison conditions not established 73.8 Model Card
Reported by a third party
Evaluated revision not stated 2026-05-12
TIGER-Lab/MMLU-Pro Task mmlu_proMetric mmlu_proComparison conditions not established 82.6 Model Card
Reported by a third party
Evaluated revision not stated 2026-04-02
cais/hle Task hleMetric hleSetup With searchComparison conditions not established 17.2 Model Card
Reported by a third party
Evaluated revision not stated 2026-04-02
llamaindex/ExtractBench Task longMetric longSetup Pipeline name: gemma4_26b_vllm_extract_oneshot_structured_output_file; self-hosted on vLLM, one-shot JSON-Schema structured outputComparison conditions not established 28.88 ExtractBench
Reported by a third party
Evaluated revision not stated 2026-08-26
llamaindex/ExtractBench Task meanMetric meanSetup Pipeline name: gemma4_26b_vllm_extract_oneshot_structured_output_file; self-hosted on vLLM, one-shot JSON-Schema structured outputComparison conditions not established 70.27 ExtractBench
Reported by a third party
Evaluated revision not stated 2026-08-26
llamaindex/ExtractBench Task mediumMetric mediumSetup Pipeline name: gemma4_26b_vllm_extract_oneshot_structured_output_file; self-hosted on vLLM, one-shot JSON-Schema structured outputComparison conditions not established 59.61 ExtractBench
Reported by a third party
Evaluated revision not stated 2026-08-26
llamaindex/ExtractBench Task shortMetric shortSetup Pipeline name: gemma4_26b_vllm_extract_oneshot_structured_output_file; self-hosted on vLLM, one-shot JSON-Schema structured outputComparison conditions not established 77.7 ExtractBench
Reported by a third party
Evaluated revision not stated 2026-08-26
llamaindex/ParseBench Task chartMetric chartSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 14.2 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14
llamaindex/ParseBench Task layoutMetric layoutSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 59.2 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14
llamaindex/ParseBench Task meanMetric meanSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 58.5 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14
llamaindex/ParseBench Task tableMetric tableSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 70 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14
llamaindex/ParseBench Task text_contentMetric text_contentSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 83.8 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14
llamaindex/ParseBench Task text_formattingMetric text_formattingSetup Pipeline name: gemma4_26b_vllm_with_layoutComparison conditions not established 65.1 ParseBench
Reported by a third party
Evaluated revision not stated 2026-04-14

SAVRN's Notes on gemma-4-26B-A4B-it

Gemma 4 26B A4B is the most downloaded Gemma 4 model in our catalog, at about 9.8 million a month. It is a mixture-of-experts design with 25.8B total parameters and roughly 4B active per token, so it answers with the speed of a small model and much of the knowledge of a large one. It reads text and images and keeps a 262,144-token context.

At 16-bit it needs about 62 GB, one large-memory accelerator; at 4-bit about 16 GB, which fits a workstation card. Apache 2.0 allows commercial use. For a team choosing one open multimodal model to self-host this year, this is near the top of our list.

SAVRN's Notes on Qwen3.6-35B-A3B-NVFP4

Unsloth's NVFP4 cut of Qwen/Qwen3.6-35B-A3B: 24.6B stated parameters in a 256-expert mixture where 8 fire per token, so the 4-bit row — 12.3 GB of weights, 14.8 GB needed — is the number that matters, and the publisher says it runs on a 32 GB VRAM GPU. Our table reads 59.1 GB at 16-bit, 29.6 GB at 8-bit, 14.8 GB at 4-bit; the cheapest listed setup, one MI300X at $1.85 an hour, leaves room for the 262,144-token context.

The publisher claims 1.56x the throughput of other NVFP4 quants, calibrated on its own dataset mixed with UltraChat, benchmarked on one B200 at 128 concurrent requests — treat those as vendor numbers and test your own prompts. Apache 2.0 covers commercial use. Mind the landscape: the same base has NVFP4 builds from Red Hat AI and Ornith in this index with different stated parameter counts, so pick by serving stack and validate the checkpoint, not the name. It draws 1.1M downloads a month.

Questions

Which is larger, gemma-4-26B-A4B-it or Qwen3.6-35B-A3B-NVFP4?

gemma-4-26B-A4B-it (25.8B parameters) is larger than Qwen3.6-35B-A3B-NVFP4 (24.6B parameters), by the parameter counts their publishers report.

Which is cheaper to run, gemma-4-26B-A4B-it or Qwen3.6-35B-A3B-NVFP4?

At 4-bit, gemma-4-26B-A4B-it fits on 1x MI300X from $1.85 an hour and Qwen3.6-35B-A3B-NVFP4 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use gemma-4-26B-A4B-it commercially?

Yes. gemma-4-26B-A4B-it is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

Can I use Qwen3.6-35B-A3B-NVFP4 commercially?

Yes. Qwen3.6-35B-A3B-NVFP4 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

Related Comparisons