# Qwen 3.6 27B vs Llama 3 vs Mistral: Full Benchmark & Hardware Comparison (June 2026) --- ## 🏆 Quick Overview | Aspect | Qwen 3.6 27B 🥇 | Mistral Small 3.1/3.2 24B | Llama 3.3 70B | |---|---|---|---| | **Best for** | Coding & multilingual | Quality-per-VRAM ratio | English reasoning | | **Min GPU** | RTX 3090/4090 (24 GB) | RTX 4060 Ti (16 GB) ✅ | Dual 3090/4090 (48 GB) | | **License** | Apache 2.0 | Apache 2.0 | Llama Community (custom) | | **Context** | 262K native | 128K | 128K | --- ## 📊 Key Benchmark Scores | Benchmark | Qwen 3.6 27B | Mistral Small 24B | Llama 3.3 70B | |---|---|---|---| | **SWE-bench Verified** | **77.2%** 🏆 | — | — | | **MMLU** | ~83% | 79–81% | **86.0%** 🏆 | | **HumanEval** | — | **92.9%** 🏆 | 88.4% | | **MATH** | ~80% 🏆 | ~65% | 77.0% | | **IFEval** (instruction following) | — | — | **92.1%** 🏆 | > **Context**: Qwen 3.6's 77.2% SWE-bench is the *best dense model score* — it resolves real GitHub pull requests. Mistral's 92.9% HumanEval beats Llama's 88.4% despite being 46B parameters smaller. --- ## 💾 VRAM Requirements (Q4_K_M Quantization) | Model | Q4_K_M VRAM | Q6_K VRAM | Min Consumer GPU | Verdict | |---|---|---|---|---| | **Qwen 3.6 27B** | ~17 GB | ~22 GB | RTX 3090/4090 (24GB) | Great fit, comfortable at short ctx | | **Mistral Small 24B** | **~13.4 GB** 🏆 | ~18 GB | **RTX 4060 Ti 16GB** ✅ | Most accessible! | | **Llama 3.3 70B** | ~40 GB ❌ | ~53 GB | Dual 3090/4090 or 48GB+ | Painful on single 24GB GPU | --- ## ⚡ Real-World Speed (Tokens/sec at Q4_K_M) | GPU | Qwen 3.6 27B | Mistral Small 24B | Llama 3.3 70B | |---|---|---|---| | **RTX 4060 Ti 16GB** | ❌ Won't fit / partial offload | **35–45 tok/s** ✅ | ❌ Won't fit | | **RTX 3090/4090 (24GB)** | ~43 tok/s | ~30–50 tok/s | 8–15 tok/s (CPU offload) ❌ | | **Dual RTX 3090/4090 (48GB)** | Fast | Fast | Comfortable ✅ | --- ## ⚖️ Trade-offs for Local Development ### 🔧 Qwen 3.6 27B — The Coding Champion **Pros:** - **77.2% SWE-bench** — best dense model for real-world code (multi-file PRs, codebase understanding) - **262K native context** — largest window of the three - **29 languages** natively supported — best multilingual option - Apache 2.0 license — no restrictions - Fits a single RTX 3090/4090 at Q4 (~17 GB) - "Thinking mode" boosts reasoning further (at cost of latency) **Cons:** - **Requires 24GB GPU** — no-go for 16GB cards - Slower than Mistral on 16GB hardware (which won't run it at all) - No native multimodal capability **Best for:** Developers doing real software engineering (multi-file edits, PRs), multilingual applications, coding-heavy workflows on a 24GB GPU. --- ### 🧠 Mistral Small 24B — The Efficiency King **Pros:** - **Fits 16GB GPUs** at Q4 (~13.4 GB) — most accessible of the three - **92.9% HumanEval** — beats Llama 3.3 70B's 88.4% on function-level coding - **35–45 tok/s** on an RTX 4060 Ti — fastest interactive experience on budget hardware - Strong European language support (French, Spanish, German, Italian, Portuguese) - Excellent function calling and tool use (targeted in instruction tuning refresh) **Cons:** - **Lower reasoning scores** — MATH 65% lags behind both competitors - **No SWE-bench scores** published — weaker at complex multi-file code changes - **128K context** — half of Qwen's 262K - MMLU 79–81% trails Llama 3.3's 86% **Best for:** Users with 16GB GPUs (most common consumer tier), interactive chat on a budget, IDE autocomplete (via Codestral variant), European-language apps. --- ### 📚 Llama 3.3 70B — The Quality Heavyweight (with a catch) **Pros:** - **86.0% MMLU** — best general knowledge & reasoning - **92.1% IFEval** — best instruction following - English-first training pipeline produces highest-quality English output - Strong at multi-step reasoning and complex prompt adherence **Cons:** - **~40 GB at Q4** — requires dual 24GB GPUs or a 48GB+ card - **8–15 tok/s** on a single 24GB GPU (CPU offloading) — miserable for chat - **Q2_K** (~20 GB) fits single 24GB card but quality degrades significantly - Only **8 supported languages** — weakest multilingual option - Custom license (not Apache 2.0) **Best for:** Users with dual GPU setups or Mac M4 Max 64GB+, English-only production reasoning tasks, applications needing the highest instruction-following accuracy. --- ## 🎯 Which Should You Choose? | Your Setup → | **RTX 4060 Ti 16GB** | **RTX 3090/4090 24GB** | **Dual 24GB or 48GB+** | |---|---|---|---| | **Coding-heavy** | Mistral Small 24B (35–45 tok/s) | **Qwen 3.6 27B** 🏆 | Qwen 3.6 27B or Llama 3.3 70B | | **General reasoning** | Mistral Small 24B | Qwen 3.6 27B | **Llama 3.3 70B** 🏆 | | **Budget/efficiency** | **Mistral Small 24B** 🏆 | Qwen 3.6 27B | Qwen 3.6 27B | | **Multilingual** | Mistral Small 24B | **Qwen 3.6 27B** 🏆 | Qwen 3.6 27B | ### Bottom Line - **Best overall on consumer hardware (24GB GPU):** **Qwen 3.6 27B** — unmatched coding, huge context, good reasoning - **Best for 16GB GPUs / budget setups:** **Mistral Small 24B** — fantastic speed-per-VRAM value - **Best for dual-GPU or pro setups:** **Llama 3.3 70B** — if you have the hardware for it, the best English reasoning *All three run via Ollama (`ollama pull qwen3 / mistral / llama3.3`). Data sourced from RunAIHome, PromptQuorum, InsiderLLM, WillItRunAI, ComputingForGeeks, and LLM Stats, June 2026.*