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TimestampReal-world Usability / Task Suitability (Is it actually usable for real tasks?)Model Name + Size + Quantization (e.g., Llama-3 8B Q4_K_M)Runtime / Stack (e.g., llama.cpp, MLX, Ollama, LM Studio)Hardware (Chip + RAM, e.g., M2 Max 64GB)Throughput (Tokens/sec)Latency / Response FeelPractical Context Window Limit
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7/25/2026 21:21:16gpt4gpt5 4b q4llaman/a2300n/an/a
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3/20/2026Yes - excellent for coding, chat, and general tasksLlama 3.3 70B Q4_K_MOllama / llama.cppM4 Max 128GB (40 GPU cores, 546 GB/s)~8-10 t/s generationNoticeable pause before response, but usable for interactive chat~8K practical (supports 128K but slows significantly beyond 8K)
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3/20/2026Yes - fast and capable for most tasksQwen 2.5 32B Q4_K_MOllama / llama.cppM4 Max 64GB (40 GPU cores, 546 GB/s)~25-30 t/s generationFast and responsive, feels near-instant~16K practical (supports 128K but quality degrades beyond 16K)
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3/20/2026Yes - best small model for coding and chatMistral Small 24B Instruct Q4_K_MOllama / llama.cppM3 Max 36GB (30 GPU cores, 300 GB/s)~18 t/s generationVery responsive, great interactive experience~8K practical (supports 32K context)
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3/20/2026Yes - excellent for quick tasks, summarization, and chatPhi-3 Mini 3.8B Q4_K_MOllama / llama.cppM1 MacBook Air 8GB (8 GPU cores, 68 GB/s)~14 t/s generationVery fast, feels instant~4K practical (supports 4K-128K depending on version)
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3/20/2026Yes - strong for coding and reasoning tasksQwen 2.5 Coder 32B Q4_K_MLM Studio / llama.cppM4 Max 64GB (40 GPU cores, 546 GB/s)~25-30 t/s generationFast, smooth streaming output~16K practical (supports 128K)
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3/20/2026Yes - good for general chat and lightweight tasksLlama 3.2 8B Q4_K_MOllamaM2 MacBook Air 16GB (10 GPU cores, 100 GB/s)~22 t/s generationFast and responsive~8K practical (supports 128K)
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3/20/2026Yes - best balance of quality and speed for M1 MacsGemma 2 9B Q4_K_MMLX / LM StudioM1 Pro 16GB (16 GPU cores, 200 GB/s)~20-25 t/s generationSmooth streaming, low latency~8K practical (supports 8K context)
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3/20/2026Yes - top-tier coding model, highly recommendedDeepSeek Coder V2 Lite 16B Q4_K_MOllama / llama.cppM2 Max 32GB (38 GPU cores, 400 GB/s)~30-35 t/s generationVery fast, excellent for interactive coding~16K practical (supports 128K)
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3/20/2026Marginally - usable for simple tasks but slow for complex workLlama 3.1 70B Q4_K_MOllama / llama.cppM1 Max 64GB (32 GPU cores, 400 GB/s)~5-6 t/s generationSlow, noticeable waiting between tokens~4K practical (context heavily impacts speed)
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3/20/2026Yes - great speed/quality for everyday useMistral 7B Instruct v0.3 Q5_K_MMLXM3 MacBook Pro 18GB (10 GPU cores, 150 GB/s)~35-40 t/s generationInstant feeling, excellent for interactive use~8K practical (supports 32K context)
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3/20/2026Yes - fastest large model experience on Apple SiliconQwen 2.5 72B Q4_K_MOllama / llama.cppM4 Max 128GB (40 GPU cores, 546 GB/s)~10-12 t/s generationModerate latency, comfortable for chat but not instant~8K practical (supports 128K but heavily impacts speed)
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3/20/2026Yes - great for summarization, writing, and RAG tasksLlama 3.1 8B Instruct Q5_K_MMLXM2 Pro 16GB (19 GPU cores, 200 GB/s)~35-40 t/s generationVery fast, feels instant~8K practical (supports 128K)
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3/20/2026No - too slow for interactive use, batch onlyLlama 3.1 70B Q4_K_MOllamaM2 MacBook Air 24GB (10 GPU cores, 100 GB/s)~1-2 t/s (model spills to swap)Extremely slow, painful wait times~2K practical (RAM insufficient, heavy swapping)
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3/20/2026Yes - excellent for creative writing, roleplay, and general chatGemma 2 27B Q4_K_MOllama / llama.cppM3 Max 36GB (40 GPU cores, 400 GB/s)~15-18 t/s generationGood responsiveness, slight initial delay~8K practical (supports 8K context)
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3/20/2026Yes - smallest viable model for basic tasks on low-RAM MacsQwen 2.5 3B Instruct Q4_K_MOllamaM1 MacBook Air 8GB (7 GPU cores, 68 GB/s)~14-16 t/s generationFast responses, limited task capability~4K practical (supports 32K)
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3/20/2026Yes - MLX delivers native Apple Silicon performanceLlama 3.1 8B Instruct 4-bit MLXMLX (mlx-community)M3 MacBook Pro 18GB (10 GPU cores, 150 GB/s)~40-50 t/s generationNear-instant, best-in-class latency for small models~8K practical (supports 128K)
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3/20/2026Yes - competitive with GPT-3.5 for many tasksMistral Small 24B Instruct Q5_K_MLM StudioM4 Pro 48GB (20 GPU cores, 273 GB/s)~20-25 t/s generationSmooth and fast, great for sustained conversations~16K practical (supports 32K context)
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3/20/2026Yes - strong for code generation and reasoningQwen 2.5 14B Instruct Q4_K_MOllama / llama.cppM2 Pro 32GB (19 GPU cores, 200 GB/s)~22-28 t/s generationFast and interactive~8K practical (supports 128K)
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3/20/2026Yes - surprisingly capable for its sizePhi-4 14B Q4_K_MOllama / llama.cppM3 Pro 18GB (14 GPU cores, 150 GB/s)~20-25 t/s generationFast, fluid streaming~8K practical (supports 16K context)
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3/20/2026Yes - best ultra-large model experience on MacQwen 2.5 72B Q4_K_MMLXM2 Ultra 192GB (76 GPU cores, 800 GB/s)~12-15 t/s generationReasonable for interactive use, slight prompt processing delay~16K practical (supports 128K)
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