Small Models Showdown: Qwen 2.5 3B vs Llama 3.2 3B vs Phi-3.5 Mini vs Gemma 2 2B
Sub-4B models have become genuinely useful. A year ago the conventional wisdom was that small models were only worth reaching for on constrained hardware where you had no other choice. That has changed. The 2B-4B size bucket now contains models that score competitively on reasoning benchmarks, handle long contexts, and generate hundreds of tokens per second even on modest hardware. For latency-sensitive applications and edge deployments, they deserve a serious look.
This post compares four of the strongest sub-4B models available in 2025: Qwen 2.5 3B, Llama 3.2 3B, Phi-3.5 Mini (3.8B), and Gemma 2 2B. We cover benchmark performance across reasoning, math, and coding, and then look at inference speed -- because at this size, throughput is often as important as capability.
The four models
Qwen 2.5 3B is Alibaba's 3 billion parameter model from the Qwen 2.5 release series. It supports a 128K context window and was trained with a heavy emphasis on code and mathematics alongside general instruction following. The instruct variant is available in a standard dense format.
Llama 3.2 3B is Meta's smallest text-only model from the Llama 3.2 family. It also supports 128K context and uses grouped-query attention (GQA) for improved inference efficiency. Meta released both base and instruction-tuned variants; this comparison uses the instruct version.
Phi-3.5 Mini is Microsoft's 3.8B parameter model from the Phi-3.5 series. It is the largest of the four models in this comparison, which matters for throughput comparisons. Phi-3.5 Mini supports 128K context and has been trained with a strong emphasis on reasoning quality relative to size. The training approach leans heavily on curated "textbook quality" synthetic data.
Gemma 2 2B is Google's smallest Gemma 2 model at 2 billion parameters. It is the most compact of the four and benefits from architectural improvements introduced in Gemma 2 -- specifically, alternating attention layers (local and global) and knowledge distillation from larger Gemma 2 models during training. Context window is 8,192 tokens.
Benchmark results
General reasoning (MMLU)
MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects from elementary math to professional law. It remains the most widely-cited general capability benchmark.
| Model | MMLU Score | |---|---| | Phi-3.5 Mini (3.8B) | ~69% | | Qwen 2.5 3B | ~65% | | Llama 3.2 3B | ~63% | | Gemma 2 2B | ~52% |
Phi-3.5 Mini leads here by a meaningful margin. The gap between it and the two 3B models is real but not enormous -- about 4-6 points. Gemma 2 2B falls noticeably behind on this benchmark, though the score is partly explained by the model being trained at a different scale with different priorities.
Math (GSM8K)
GSM8K measures grade-school math word problem solving. It is a good proxy for multi-step reasoning accuracy in the small model regime.
| Model | GSM8K Score | |---|---| | Phi-3.5 Mini (3.8B) | ~86% | | Qwen 2.5 3B | ~79% | | Llama 3.2 3B | ~77% | | Gemma 2 2B | ~40% |
Math is where the gap widens. Phi-3.5 Mini at ~86% is substantially ahead of both 3B models, and Gemma 2 2B drops sharply. Qwen 2.5 3B and Llama 3.2 3B are close, with Qwen edging ahead slightly -- consistent with Qwen's stated focus on math during training.
Coding (HumanEval)
HumanEval tests Python code generation through 164 programming problems. It measures whether the model can write functionally correct code from a docstring.
| Model | HumanEval Pass@1 | |---|---| | Qwen 2.5 3B | ~65% | | Phi-3.5 Mini (3.8B) | ~62% | | Llama 3.2 3B | ~45% | | Gemma 2 2B | ~20% |
Coding is Qwen 2.5 3B's clearest win. It leads HumanEval by a few points over Phi-3.5 Mini and opens a larger gap over Llama and Gemma. If your application involves generating or reasoning about code, Qwen 2.5 3B is the small model to use.
Llama 3.2 3B's coding score is notably lower than its general reasoning score -- it is a solid general model that was not optimized specifically for code.
Context length
Three of the four models support 128K context:
- Qwen 2.5 3B: 128K
- Llama 3.2 3B: 128K
- Phi-3.5 Mini: 128K
- Gemma 2 2B: 8K
The 8K limit on Gemma 2 2B is a hard constraint that eliminates it from any use case requiring long documents, multi-turn conversation histories beyond a few turns, or large retrieval contexts. The 128K support on the other three models is genuine -- all three were trained with rope scaling for extended context.
Inference speed
Benchmark scores only matter in the context of how fast a model generates output in practice. At the sub-4B size bucket, throughput is a core differentiator.
Inference speed is a function of parameter count, architecture, and the precision used (FP16, BF16, INT8, INT4). On a single A100 80GB with FP16 precision:
| Model | Approx tokens/sec (single GPU, FP16) | |---|---| | Gemma 2 2B | ~800-1000 | | Qwen 2.5 3B | ~550-700 | | Llama 3.2 3B | ~500-650 | | Phi-3.5 Mini (3.8B) | ~420-550 |
Gemma 2 2B is the fastest by a significant margin, which follows directly from having fewer parameters. Qwen 2.5 3B and Llama 3.2 3B are roughly similar, with minor differences depending on batch size and sequence length. Phi-3.5 Mini is the slowest of the group at 3.8B.
On managed inference infrastructure like GeneralCompute's ASIC-optimized API, all four models run substantially faster than on self-hosted GPU. The ordering holds -- Gemma 2 2B still leads on throughput -- but the absolute speeds are much higher, making latency less of a constraint even for the larger models in this bucket.
Summary table
| | Qwen 2.5 3B | Llama 3.2 3B | Phi-3.5 Mini | Gemma 2 2B | |---|---|---|---|---| | MMLU | ~65% | ~63% | ~69% | ~52% | | GSM8K | ~79% | ~77% | ~86% | ~40% | | HumanEval | ~65% | ~45% | ~62% | ~20% | | Context | 128K | 128K | 128K | 8K | | Relative speed | Fast | Fast | Moderate | Fastest | | Params | 3B | 3B | 3.8B | 2B |
When to use each model
Use Qwen 2.5 3B when coding is the primary task. It leads HumanEval by a clear margin among 3B-class models, and its math scores are strong enough for most applications. The 128K context window makes it viable for code review, document summarization, and longer conversation loops. If you are building a small coding assistant, code explanation tool, or anything involving generation of structured data and code, Qwen 2.5 3B is the best choice in this size range.
Use Phi-3.5 Mini when reasoning accuracy matters more than throughput. It scores highest on both MMLU and GSM8K, which reflects its training on high-quality reasoning data. The 3.8B parameter count means it is moderately slower than the 3B models, but the capability gap on complex reasoning tasks is real. Phi-3.5 Mini is a good choice for analytical tasks, question answering over documents, and any use case where you want the best general capability from a small model.
Use Llama 3.2 3B as a general-purpose baseline. It is not the top scorer in any category, but it is close to Qwen 2.5 3B on reasoning and math while offering the ecosystem advantages of the Llama family: wide framework support, extensive community fine-tunes, and well-tested deployment tooling. If you want to avoid committing to any specific model family, Llama 3.2 3B is a safe default.
Use Gemma 2 2B for throughput-constrained applications. It is the fastest model in this group by a meaningful margin, and its quality is competitive for simpler tasks. The 8K context limit is a real constraint, so it does not work for long-context applications. But for classification, short-form generation, summarization of short texts, or any use case where you need to process a very high volume of short requests, Gemma 2 2B's throughput advantage is significant.
Quantization and deployment notes
All four models are available in quantized formats, which meaningfully affects this comparison in production:
INT4 quantization cuts memory usage roughly in half. A 3B FP16 model uses about 6GB of VRAM; in INT4 it fits in 2-3GB, which means it runs on consumer GPUs or even some edge devices. Quality loss with INT4 is usually small but noticeable on math tasks -- expect a few points of regression on GSM8K.
GGUF format (used by llama.cpp) makes these models accessible on CPU. Gemma 2 2B and the 3B models are fast enough in GGUF INT4 on a modern laptop CPU to be usable for offline applications. Phi-3.5 Mini at 3.8B is slower on CPU but still functional.
For server-side deployment, vLLM, TGI, and SGLang all support these models natively. If you are using an API provider, all four are widely available through hosted inference endpoints.
A note on fine-tuning
One advantage of small models that is easy to overlook: they are much cheaper to fine-tune than 7B or 70B models. A QLoRA fine-tune of a 3B model on a single A100 takes an hour or two and produces a task-specific model that can outperform the base model by a large margin on a narrow domain.
For production applications where you have labeled data and want to push beyond what the base model achieves, fine-tuning a small model is often faster and cheaper than prompting a larger one. The fine-tuned 3B model also remains fast at inference time, which matters if you care about cost-per-query.
Conclusion
The sub-4B size bucket now has strong options for a range of use cases. Phi-3.5 Mini leads on general reasoning and math. Qwen 2.5 3B leads on coding. Gemma 2 2B leads on throughput. Llama 3.2 3B is a solid general baseline with broad ecosystem support.
The right model depends on your task distribution and latency requirements. For applications where the primary bottleneck is throughput and tasks are relatively simple, Gemma 2 2B or one of the 3B models running at high tokens-per-second is often the right answer. For tasks that require reliable reasoning, Phi-3.5 Mini is worth the moderate speed trade-off.
GeneralCompute hosts all of the models in this comparison with an OpenAI-compatible API, giving you fast inference across the full sub-4B range without managing infrastructure:
from openai import OpenAI client = OpenAI( base_url="https://api.generalcompute.com/v1", api_key="your-api-key", ) response = client.chat.completions.create( model="qwen2.5-3b-instruct", # or llama-3.2-3b, phi-3.5-mini, gemma-2-2b-it messages=[{"role": "user", "content": "Explain binary search in Python with an example."}], ) print(response.choices[0].message.content)
See the GeneralCompute model catalog for available models, current pricing, and latency benchmarks.