5 Open-Weight LLMs Compared: Llama, Mistral, Qwen, Phi, Gemma

Why Compare These 5 Model Families?

The open-weight LLM landscape has exploded with viable options from major AI labs. Each family makes different trade-offs between parameter count, context window, inference speed, and reasoning capability. Whether you're deploying on edge devices, building a coding assistant, or processing multilingual documents, these five families represent the current Pareto frontier - and all can run locally on consumer hardware. Understanding their specs is essential before downloading and deploying.

Comparison Table

Model Family	Parameter Sizes	Max Context	License	Publisher	Standout Strength
Llama 3.2	1B, 3B, 11B*, 90B*	128K	Llama Community	Meta	On-device deployment (1B/3B)
Mistral/Mixtral	7B, 8x7B (46.7B total)	32K	Apache 2.0	Mistral AI	Code generation & inference speed
Qwen 2.5	0.5B - 72B	128K (1M Turbo)	Apache 2.0	Alibaba	Long-context handling, multilingual
Phi-4	3.8B, 14B, 5.6B*	16K, 128K*	MIT	Microsoft	Reasoning quality per parameter
Gemma 2	2B, 9B, 27B	8K	Gemma Terms	Google	Efficient dense models

*Vision/multimodal variants. Llama 3.2 11B/90B add image understanding; Phi-4 multimodal adds 128K context; Gemma plans 256K in Gemma 4.

Llama 3.2 in Brief

Llama 3.2 includes small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B). The 1B and 3B models support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.

Key specs: Trained with up to 9 trillion tokens and support the long context length of 128k tokens. Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement) with a requirement to request a license from Meta if monthly active users exceed 700 million.

Best for: Mobile deployment, summarization, edge AI, instruction-following tasks on resource-constrained devices.

Mistral / Mixtral in Brief

Mistral is a 7B parameter language model, focused on balancing the scaling costs of large models with performance and efficient inference. In contrast, Mixtral has 46.7B total parameters but only uses 12.9B parameters per token, processing input and generating output at the same speed and for the same cost as a 12.9B model.

Key specs: Mixtral gracefully handles a context of 32k tokens. Both are licensed under Apache 2.0. Mixtral shows strong performance in code generation.

Best for: Code generation, efficient inference on modest GPUs, cost-per-token optimization, multilingual workflows.

Qwen 2.5 in Brief

The Qwen 2.5 release includes seven open-sourced models with sizes ranging from 0.5B to 72B. Most models support a context length of 128K (131,072) tokens and can generate up to 8K tokens.

Key specs: Training on 18 trillion tokens and sophisticated post-training techniques, including supervised fine-tuning and multi-stage reinforcement learning. The Qwen models are released under the Apache License 2.0, a permissive open source license that allows free use, modification, and distribution, including commercial use, requiring preservation of copyright notices and disclaimers. Extended context available: Qwen2.5-1M is the long-context version supporting a context length of up to 1M tokens.

Best for: Multilingual applications, long-context document processing, instruction-following at scale (72B variant), dense reasoning.

Phi-4 in Brief

Microsoft Phi-4 is a 14 billion parameter decoder-only Transformer model, developed to deliver advanced reasoning capabilities efficiently, enabling deployment in environments with limited compute and memory. Phi-4 supports a 16,000-token context length, allowing it to process and generate extensive long-form content.

Key specs: Trained on approximately 10 trillion (10T) tokens. Phi-4 comes with a permissive MIT License, allowing it to be used for commercial applications. A key innovation lies in its strategic use of high-quality synthetic data, which constitutes a significant portion of its training corpus.

Best for: Mathematical reasoning, complex logic problems, scientific question-answering, resource-constrained commercial deployments.

Gemma 2 in Brief

Gemma 2 comes in two sizes, 9 billion and 27 billion parameters with base (pre-trained) and instruction-tuned versions. The Gemma 2 models were trained on approximately 2x more data than their first iteration, totaling 13 trillion tokens for the 27B version and 8 trillion tokens for the 9B version of web data (primarily English), code, and math.

Key specs: Context length of 8192 tokens, use of Rotary Position Embeddings (RoPE). Gemma 2 comes with a permissive license that allows redistribution, fine-tuning, commercial use, and derivative works (custom Gemma Terms of Use). Sliding window attention is applied to every other layer (local - 4096 tokens), while the layers in between still use full quadratic global attention (8192 tokens), a way to increase quality in long context situations while partially benefiting from the advantages of sliding attention.

Best for: Balanced general-purpose use, on-device deployment (2B), efficient inference on TPUs/GPUs, education and research.

Which Model Should You Choose?

For modest hardware (4-8GB VRAM or less):

• Llama 3.2 1B if you need edge/mobile deployment
• Phi-4-mini (3.8B) for better reasoning in same footprint
• Gemma 2 2B for lightweight instruction-tuned chat

For mid-range hardware (12-24GB VRAM):

• Qwen 2.5 7B or Llama 3.2 3B for long-context tasks
• Phi-4 14B for mathematical reasoning and coding
• Gemma 2 9B for general-purpose, well-rounded performance

For serious coding tasks:

• Mixtral 8x7B for the best code generation with efficient inference
• Qwen 2.5 32B-72B for complex code generation plus reasoning
• Phi-4 14B for math-heavy algorithm design

For multilingual or cross-language work:

• Qwen 2.5 (explicitly trained on 29+ languages)
• Mistral 7B/Mixtral (supports French, German, Spanish, Italian, English)
• Avoid Gemma 2 unless English-primary is acceptable

For maximum context length:

• Qwen 2.5-Turbo or Qwen 2.5-1M (up to 1M tokens, production-ready at 128K)
• Qwen 2.5 128K variants (7B-72B)
• Phi-4 variants up to 128K (multimodal version)

For commercial deployments with licensing clarity:

• Phi-4 (MIT - zero restrictions beyond attribution)
• Mistral/Mixtral (Apache 2.0 - permissive, no user thresholds)
• Qwen 2.5 (Apache 2.0 - same as Mistral)
• Avoid: Llama (700M MAU threshold), Gemma (custom Terms with flow-down obligations)

For pure efficiency (inference speed + low VRAM):

• Mistral 7B (sliding window attention)
• Mixtral 8x7B (sparse MoE - 13B active per token)
• Phi-4-mini 3.8B (dense, high-quality data)

FAQ

Can I use these models commercially without restrictions?

Not equally. Phi-4 comes with a permissive MIT License, allowing it to be used for commercial applications. Mistral, Mixtral, and Qwen 2.5 all use Apache 2.0 (no restrictions). Llama 3.2 allows commercial use but requires a separate license from Meta if your product exceeds 700 million monthly active users. Gemma 2 allows commercial deployment but under a custom Gemma Terms of Use, not a standard open-source license.

Which model runs on consumer GPUs like RTX 4090 or RTX 4070?

Phi-4 Multimodal with 5.6B parameters and 128K token context length is optimized for both on-device execution and low-latency inference. Qwen 2.5 7B-14B, Mistral 7B, Llama 3.2 3B, and Gemma 2 9B all quantize to fit 12-24GB VRAM efficiently. For best compatibility, use 4-bit or 8-bit quantization (GGUF, AWQ, or bitsandbytes format).

How do context lengths actually compare for real document work?

Most Qwen 2.5 models support a context length of 128K (131,072) tokens and can generate up to 8K tokens, matching Llama 3.2. Mistral and Mixtral cap at 32K. Gemma 2 is limited to 8K, making it unsuitable for full-document processing; Phi-4's 16K is better but still modest. For production long-context use, Qwen 2.5 is the clear winner.

Is synthetic training data (Phi-4) better than diverse internet data?

It depends on the task. Phi-4's strategic use of high-quality synthetic data, generated using techniques such as multi-agent prompting, instruction reversal, and self-revision workflows, enables it to acquire strong reasoning and problem-solving abilities, often surpassing models with larger parameter counts. This works exceptionally well for math, logic, and code - less so for open-ended creative or factual tasks where Qwen 2.5 or Llama's broad training shines.