SAVRN Model Hub · Comparisons
gemma-4-31B-it vs Qwen2.5-VL-32B-Instruct
Gemma-4-31B-it has 31.3B parameters and Qwen2.5-VL-32B-Instruct has 33.5B parameters; both are released under Apache License 2.0; at 16-bit, gemma-4-31B-it needs about 75.1 GB (1x MI300X from $1.85 an hour) and Qwen2.5-VL-32B-Instruct about 80.3 GB (1x MI300X from $1.85 an hour).
| Field | gemma-4-31B-it google/gemma-4-31B-it | Qwen2.5-VL-32B-Instruct Qwen/Qwen2.5-VL-32B-Instruct |
|---|---|---|
| Publisher | Qwen | |
| Task | Image and text to text | Image and text to text |
| Modality | Image and text | Image and text |
| Parameters, as reported | 31.3B parameters | 33.5B parameters |
| Architecture | Gemma4ForConditionalGeneration | Qwen2_5_VLForConditionalGeneration |
| Library | transformers | transformers |
| Context length | 262,144 tokens | 128,000 tokens |
| Repository size | 62.6 GB | 68.3 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) | 75.1 GB | 80.3 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 18.8 GB | 20.1 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 842da3794eaa | 7cfb30d71a1f |
| Downloads reported by the hub | 9M | 1.1M |
| Last observed | 2026-09-18 | 2026-09-18 |
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-4-31B-it
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Idavidrein/gpqa | Task diamondMetric diamondComparison conditions not established | 84.3 | Model Card Reported by a third party |
Evaluated revision not stated | 2026-04-02 |
| LiquidAI/ifstruct-v1.0 | Task ifstruct_v1Metric ifstruct_v1Comparison conditions not established | 95.9 | Liquid AI — IFStruct v1.0 blog (gemma-4-31B-it) Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
| MMMU/MMMU_Pro | Task mmmu_pro_visionMetric mmmu_pro_visionComparison conditions not established | 76.9 | 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 | 85.2 | Model Card Reported by a third party |
Evaluated revision not stated | 2026-04-02 |
| cais/hle | Task hleMetric hleSetup With searchComparison conditions not established | 26.5 | Model Card Reported by a third party |
Evaluated revision not stated | 2026-04-02 |
| joelniklaus/LEXam-hard | Task lexam_hardMetric lexam_hardSetup lighteval, LEXam paper prompts, one response per question, no tools; DeepSeek-R1-0528 judge; mean of the German and English means over the 518 questions, 0-100Comparison conditions not established | 37.41 | SwissLegalEvals per-sample details (lighteval) Reported by a third party |
Evaluated revision not stated | 2026-06-11 |
| llamaindex/ParseBench | Task chartMetric chartSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 15 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| llamaindex/ParseBench | Task layoutMetric layoutSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 57.4 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| llamaindex/ParseBench | Task meanMetric meanSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 62.4 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| llamaindex/ParseBench | Task tableMetric tableSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 80.6 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| llamaindex/ParseBench | Task text_contentMetric text_contentSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 89.9 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| llamaindex/ParseBench | Task text_formattingMetric text_formattingSetup Pipeline name: gemma4_31b_vllm_with_layoutComparison conditions not established | 69.3 | ParseBench Reported by a third party |
Evaluated revision not stated | 2026-04-17 |
| sapbot/ask-my-agent-bench-2 | Task fallbackMetric fallbackComparison conditions not established | 72.1 | Not named Reported by a third party |
Evaluated revision not stated | 2026-06-23 |
SAVRN's Notes on gemma-4-31B-it
One MI300X at $1.85 per hour carries this 31.3 billion parameter Gemma at 16-bit: 62.5 GB of weights and 75.1 GB needed against the card's 192 GB. Quantizing brings that to 37.5 GB at 8-bit and 18.8 GB at 4-bit, but here it buys concurrent context, not fit. With 262,144 tokens of context and image input, we would stay at 16-bit and give the spare memory to long documents.
Apache 2.0 permits commercial use, modification and redistribution, provided the license and NOTICE file stay attached and significant changes are stated. It is the instruction-tuned build of google/gemma-4-31B, described at arXiv:2607.02770; audio input belongs to the E2B, E4B and 12B sizes, not this one. DeepInfra hosts it at $0.13 in and $0.38 out per million tokens, Novita at $0.14 and $0.40; the output rate is where your own facility makes its case.
SAVRN's Notes on Qwen2.5-VL-32B-Instruct
Run it at 16-bit and you need 80.3 GB, so precision is the hardware decision. At 8-bit it needs 40.1 GB, at 4-bit 20.1 GB. The cheapest setup on our board is one 192 GB MI300X at $1.85 per hour on-demand, and at 16-bit the weights alone take 66.9 GB of that card before context loads. Images and text go in, text comes out. Qwen ships the 33.5B parameters as 32 safetensors files, about 68.3 GB, so plan the storage pull first.
Apache 2.0 covers commercial use, modification and redistribution, provided notices stay and significant changes are stated, which suits a deployment inside your facility. Check the context: 128,000 tokens is the ceiling, the configuration lists a 32,768 token sliding window, and the page cites the YaRN paper on context window extension, arXiv 2309.00071, so test with your longest real inputs before committing. The technical report is arXiv 2502.13923.
Questions
Which is larger, gemma-4-31B-it or Qwen2.5-VL-32B-Instruct?
Qwen2.5-VL-32B-Instruct (33.5B parameters) is larger than gemma-4-31B-it (31.3B parameters), by the parameter counts their publishers report.
Which is cheaper to run, gemma-4-31B-it or Qwen2.5-VL-32B-Instruct?
At 4-bit, gemma-4-31B-it fits on 1x MI300X from $1.85 an hour and Qwen2.5-VL-32B-Instruct on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use gemma-4-31B-it commercially?
Yes. gemma-4-31B-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 Qwen2.5-VL-32B-Instruct commercially?
Yes. Qwen2.5-VL-32B-Instruct 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.