SAVRN Model Hub · Comparisons
Llama-3.2-1B-Instruct vs Qwen2.5-1.5B-Instruct
Llama-3.2-1B-Instruct has 1.2B parameters and Qwen2.5-1.5B-Instruct has 1.5B parameters; Llama-3.2-1B-Instruct is released under llama3.2 and Qwen2.5-1.5B-Instruct under Apache License 2.0; at 16-bit, Llama-3.2-1B-Instruct needs about 3 GB (1x MI300X from $1.85 an hour) and Qwen2.5-1.5B-Instruct about 3.7 GB (1x MI300X from $1.85 an hour).
| Field | Llama-3.2-1B-Instruct meta-llama/Llama-3.2-1B-Instruct | Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct |
|---|---|---|
| Publisher | Meta Llama | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 1.2B parameters | 1.5B parameters |
| Architecture | LlamaForCausalLM | Qwen2ForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 32,768 tokens |
| Repository size | 5.0 GB | 3.1 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | llama3.2 | apache-2.0 |
| Access | Access requested at publisher | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 3 GB | 3.7 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.7 GB | 0.9 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 9213176726f5 | 989aa7980e4c |
| Downloads reported by the hub | 7.4M | 6.7M |
| Last observed | 2026-10-09 | 2026-10-09 |
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.
Llama-3.2-1B-Instruct
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Idavidrein/gpqa | Task diamondMetric diamondSetup GPQA DiamondComparison conditions not established | 18.6869 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-04-16 |
SAVRN's Notes on Llama-3.2-1B-Instruct
1.2B parameters at 3.0 GB in 16-bit memory, 0.7 GB at 4-bit: cheap to run on the one 192 GB MI300X at $1.85 an hour we list as the cheapest setup — but access is gated, the llama3.2 license is Meta's own rather than Apache or MIT, and the page records no context length, so verify that with your own tests before promising anything.
Meta frames the 1B and 3B instruct pair as multilingual dialogue engines — English, German, French, Italian, Portuguese, Hindi, Spanish and Thai are listed — tuned with SFT and RLHF for agentic retrieval and summarization. The single evaluation on the page, GPQA Diamond at 18.6869, sets the quality ceiling. Weights ship as pth plus safetensors across 13 files totalling 5.0 GB; one attached paper covers SpinQuant quantization, worth reading before you run 4-bit.
SAVRN's Notes on Qwen2.5-1.5B-Instruct
1.5B parameters, 3.7 GB at 16-bit, 0.9 GB at 4-bit, with 32,768 tokens of context: the smallest Qwen2.5 that still handles general instruction traffic, and one 192 GB MI300X at $1.85 an hour would carry more than fifty copies of the 16-bit footprint. Packing density, not capacity, is what sets your cost per instance. Downloads of 7.7M a month slot between the 0.5B's 8.7M and the 7B's 9.7M.
Apache 2.0 permits commercial use, fine-tuning and redistribution, notices kept. The single 3.1 GB safetensors file and weights dated September 2024 make this the cheapest credible substrate for domain tuning — the derived list runs to agriculture, health, fraud and code-infused community builds, several shipped as GGUF Q4_K_M and IQ3_XXS conversions. Search it before training your own; the license lets you ship whatever you fine-tune.
Questions
Which is larger, Llama-3.2-1B-Instruct or Qwen2.5-1.5B-Instruct?
Qwen2.5-1.5B-Instruct (1.5B parameters) is larger than Llama-3.2-1B-Instruct (1.2B parameters), by the parameter counts their publishers report.
Which is cheaper to run, Llama-3.2-1B-Instruct or Qwen2.5-1.5B-Instruct?
At 4-bit, Llama-3.2-1B-Instruct fits on 1x MI300X from $1.85 an hour and Qwen2.5-1.5B-Instruct on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Qwen2.5-1.5B-Instruct commercially?
Yes. Qwen2.5-1.5B-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.