Key Takeaways
- Coding: Qwen 3.6 27B leads SWE-bench (77.2% real-world, best dense model). For agentic coding: Mistral Devstral Small 24B. For IDE autocomplete: Mistral Codestral 22B.
- General reasoning: Llama 3.3 70B and Qwen3 72B remain nearly tied; Llama 3.x is stronger in English, Qwen in multilingual.
- Efficiency (quality per GB of RAM): Mistral Small 3.1 24B delivers near-70B quality at 14 GB RAM -- unchanged since April.
- Languages beyond English: Qwen3 supports 29 languages natively; Llama and Mistral are primarily English-optimized.
- MoE efficiency (new in 2026): Llama 4 Scout (17B active / 109B total) runs on ~10 GB VRAM with 10M token context -- the biggest paradigm shift in this comparison.
- Legacy models still relevant: Mistral Small 24B, Qwen 2.5 14B, and Llama 3.1 8B remain widely deployed. The "Legacy Benchmark Reference" section below covers when to upgrade vs when to stay.
•Info: 📌 Looking for the older comparison? Jump to Mistral 24B vs Qwen 2.5 14B vs Llama 3.1 8B Legacy Benchmarks below.
Which Open-Weight Model Family Should You Choose?
Previous generation models (Qwen3, Llama 3.3) remain available on Ollama and are still widely used. This comparison focuses on current-generation models.
| Family | Developer | Current Releases | Licence |
|---|---|---|---|
| Qwen3 | Alibaba | Qwen3 (April 2026), Qwen 3.5 (multimodal), Qwen 3.6 27B (SWE-bench 77.2%) | Apache 2.0 (most sizes) |
| Llama 4 | Meta | Scout (17B active/109B MoE, 10M ctx), Maverick (17B active/400B MoE), Legacy: 3.3 70B | Llama Community (custom) |
| Mistral | Mistral AI | Small 3.1 (24B), Devstral Small 24B (agentic), Codestral 22B (FIM/IDE) | Apache 2.0 (most sizes) |
How Do These Models Compare on Benchmarks?
SWE-bench (real-world GitHub issue resolution) is the primary 2026 coding benchmark for practical coding evaluation. It tests multi-file changes, codebase understanding, and test writing. HumanEval (single-function Python) remains useful for comparison but is secondary. MMLU and MATH evaluate general knowledge and reasoning. Llama 4 Scout benchmarks are limited due to recent release and MoE complexity. Dashes indicate benchmarks not yet published or not applicable.
| Model | MMLU | SWE-bench | MATH | RAM (Q4_K_M) |
|---|---|---|---|---|
| Qwen 3.6 27B | ~83% | 77.2% | ~80% | 16 GB |
| Qwen3 72B | ~85% | — | ~84% | 43 GB |
| Llama 4 Scout 17B (MoE) | — | — | — | ~10 GB |
| Llama 3.3 70B (legacy) | 82% | — | 77% | 40 GB |
| Mistral Small 3.1 24B | 79% | — | 65% | 14 GB |
| Devstral Small 24B | — | High (agentic) | — | 16 GB |
| Qwen3 8B | ~75% | — | ~55% | 5 GB |
| Mistral 7B v0.3 | 64% | — | 28% | 4.5 GB |
Which Tasks Does Qwen3 / Qwen 3.6 Excel At?
Qwen3 (April 2026) and Qwen 3.6 (May 2026) from Alibaba lead on coding benchmarks. Qwen 3.6 27B scores 77.2% SWE-bench — the best dense coding model available. Qwen3 72B continues to lead on MMLU at ~85%. Qwen 3.5 adds multimodal capabilities. The Qwen3 family includes both dense models and MoE variants (35B-A3B).
Strengths: coding (Python, JavaScript, SQL, SWE-bench leading), mathematical reasoning (84% MATH at 72B), 29-language native support, JSON mode, function calling, 128K context window across all sizes.
Weaknesses: English instruction-following style can feel less natural than Llama or Mistral; some users report less fluent creative writing in English. The Alibaba origin raises data-handling concerns for some enterprise users despite open weights.
Why Is Llama 4 the Most Versatile?
Llama 4 (April 2025) introduced MoE architecture to the Llama family. Scout (17B active / 109B total) fits on 12 GB VRAM with a 10M token context window — the largest context of any locally-runnable model. Maverick (17B active / 400B total) targets multi-GPU setups. Llama 3.3 70B remains the most battle-tested dense model, but Llama 4 Scout offers better quality per VRAM on most tasks.
Strengths: 10M context window (Scout), MoE efficiency (17B active params at 12 GB VRAM), strongest English instruction-following and creative writing, ecosystem support remains widest of any open-source family, Llama 3.3 70B still widely fine-tuned.
Weaknesses: no native multilingual support (Qwen3 still leads for non-English by a wide margin); Llama 4 Scout benchmarks still emerging. Llama 3.3 70B and Llama 3.1 8B remain available and are still the most widely fine-tuned base models.
What's Mistral's Biggest Advantage?
Mistral AI produces the most parameter-efficient models in this comparison and now offers specialized variants. Mistral Small 3.1 at 24B delivers benchmark scores close to the 70B class while requiring only 14 GB RAM -- the best quality-per-RAM ratio. Devstral Small 24B (Mistral AI, 2026) is purpose-built for agentic coding — multi-file edits, tool calling, and debugging loops. Codestral 22B is Mistral's FIM-optimized model for IDE autocomplete — the recommended model for Continue.dev and Cursor integrations.
Strengths: best quality-to-RAM ratio (Small 3.1), Devstral for agentic coding, Codestral for IDE/FIM, strong function calling and tool use, clean Apache 2.0 licence on key models, European provenance (France) for EU AI Act compliance.
Weaknesses: Mistral 7B v0.3 is now outperformed on benchmarks by Qwen3 7B and Llama 3.1 8B; fewer size options at the frontier than Qwen or Llama (though specialization partially offsets this).
Tool Calling and Reasoning Comparison
Tool calling (function calling) allows a model to invoke external APIs and tools in agentic workflows. As of April 2026, all three families support it natively.
| Model | Tool Calling | Reasoning (MATH) | Best For |
|---|---|---|---|
| Qwen3 72B | ✅ Native | 83% | Complex multi-step agents |
| Llama 3.3 70B | ✅ Native | 77% | English-first agent workflows |
| Mistral Small 3.1 24B | ✅ Native, well-tested | 65% | Production tool use at 16 GB |
| Qwen3 14B | ✅ Native | 70% | Cost-effective tool calling |
| Llama 3.2 3B | ✅ Native | 51% | Lightweight agents |
| Mistral 7B v0.3 | ⚠️ Limited | 28% | Not recommended for tool use |
For reasoning-heavy tasks (math, logic, code review): DeepSeek-R1 (MIT licence, 7B-32B) outperforms all three families on MATH benchmarks. Consider it alongside these three for analytical workflows.
Which Model Family Wins by Task?
Model choice is step one; prompt design is step two. The same prompt can produce vastly different results across Qwen, Llama, and Mistral. For systematic techniques to get consistent results from any model family, see the prompt engineering guide.
| Task | Winner | Why |
|---|---|---|
| Python / JavaScript coding (generation) | Qwen 3.6 | 77.2% SWE-bench — best dense coding model |
| Agentic coding (multi-file, debugging) | Mistral (Devstral) | Purpose-built for agentic workflows |
| IDE autocomplete (FIM) | Mistral (Codestral) | FIM-optimized, Continue.dev/Cursor support |
| General Q&A (English) | Llama 3.3 / Qwen3 (tied) | Both score 82-85% MMLU at 70B |
| Mathematical reasoning | Qwen3 | 84% MATH at 72B vs 77% for Llama 3.3 70B |
| Non-English languages | Qwen3 | 29 native languages; Llama and Mistral are English-primary |
| Creative writing (English) | Llama 3.x/4 | More natural English generation style |
| Quality on 16 GB RAM | Mistral Small 3.1 | Near-70B quality at 14 GB RAM — unchanged |
| Long-context tasks (10M+ tokens) | Llama 4 Scout | 10M token context window — no competitor matches |
| Beginner first model | Llama 4 3B | Best documented, most community support — unchanged |
How Do Models Compare at the Same Scale?
3B-4B class: Qwen3 3B and Phi-4 Mini 3.8B outperform Llama 4 3B on coding and math. For general English use, Llama 4 3B is more reliable.
7B-8B class: Qwen3 8B (~5 GB) and Llama 3.1 8B (~5.5 GB) both significantly outperform Mistral 7B v0.3. Qwen3 8B leads on coding; Llama 3.1 8B leads on English instruction-following.
14B-24B class: Qwen3 14B and Mistral Small 3.1 24B are the primary options. Mistral Small 3.1 is stronger overall despite requiring more RAM. Devstral Small 24B is the best choice for developers doing agentic coding at this tier.
MoE class (new in 2025-2026): Llama 4 Scout (17B active / 109B total) and Qwen3 35B-A3B (3B active / 35B total) use Mixture-of-Experts architecture — only a fraction of parameters activate per token. This makes them dramatically more VRAM-efficient than dense models. Llama 4 Scout at ~10 GB VRAM outperforms most dense 13B models. MoE models are the biggest architectural shift since the article's original comparison was written.
70B-72B class: Llama 3.3 70B and Qwen3 72B are the best locally-runnable dense models in 2026. Choose Qwen3 72B for coding and multilingual; choose Llama 3.3 70B for English-first general tasks.
Qwen, Llama, and Mistral cover the open-source landscape. For a comparison that includes commercial alternatives — GPT-4o, Claude Sonnet 4.6, and Gemini 3.1 Pro — and when to choose proprietary over open-source, see how to pick the right AI model.
Mistral Small 24B vs Qwen 2.5 14B vs Llama 3.1 8B: Legacy Benchmark Reference
Many developers still run the previous generation: Mistral Small 24B (2024), Qwen 2.5 14B (2024), and Llama 3.1 8B (2024). These models remain available on Ollama and are widely deployed in production. This section compares them directly for teams who haven't upgraded yet, and explains when upgrading to Qwen 3, Llama 4, or current Mistral makes sense.
- Mistral Small 24B delivers the highest absolute benchmarks of the three but requires 14 GB RAM. Best for 16 GB+ machines where quality matters more than headroom.
- Qwen 2.5 14B is the strongest coding model in this legacy tier, scoring 71% HumanEval at 8 GB RAM. Best for developers on 12-16 GB RAM machines who prioritize code generation.
- Llama 3.1 8B has the broadest ecosystem support — most fine-tunes, most tutorials, most community help. Best for first-time users or teams that need broad community resources.
- When to upgrade Mistral Small 24B → Mistral Small 3.1 24B: if you need agentic coding (use Devstral Small 24B), IDE autocomplete (use Codestral 22B), or incremental quality improvements at same RAM footprint.
- When to upgrade Qwen 2.5 14B → Qwen 3 14B or Qwen 3.6 27B: if you need SWE-bench performance (Qwen 3.6 27B scores 77.2%, the best dense coding model in 2026), already on 16 GB RAM, or need 29-language native support (Qwen 3 expanded multilingual coverage).
- When to upgrade Llama 3.1 8B → Llama 4 Scout: if you have 12 GB+ VRAM (Scout's MoE architecture activates 17B/109B params, ~10 GB VRAM), need long-context reasoning (Scout supports 10M tokens vs Llama 3.1's 128K), or want frontier-class performance per VRAM (Scout outperforms most dense 13B models).
- Stay on legacy models if: your fine-tunes are built on Llama 3.1 8B or Qwen 2.5 (migration cost > benefit), production stability matters more than benchmarks (legacy models are battle-tested), or your workload doesn't require the new capabilities (general chat, summarization, basic Q&A).
- Quick decision matrix for legacy users:
- • Have 8 GB RAM, doing general chat: Stay on Llama 3.1 8B or Mistral 7B v0.3.
- • Have 12-16 GB RAM, doing coding: Upgrade Qwen 2.5 14B → Qwen 3 14B or Qwen 3.6 27B.
- • Have 16+ GB RAM, want best quality: Upgrade Mistral 24B → Mistral Small 3.1 24B (general) or Devstral 24B (agentic coding).
- • Have 12+ GB VRAM: Skip dense models entirely — use Llama 4 Scout (MoE, 10M context) for the best quality-per-VRAM ratio in 2026.
| Model | Parameters | RAM (Q4_K_M) | MMLU | HumanEval | Best For |
|---|---|---|---|---|---|
| Mistral Small 24B | 24B dense | 14 GB | 79% | 73% | Best quality per RAM (legacy tier) |
| Qwen 2.5 14B | 14B dense | 8 GB | 73% | 71% | Coding on mid-range hardware |
| Llama 3.1 8B | 8B dense | 5 GB | 68% | 65% | Most documented, easiest start |
Regional Context: Which Family for EU, Japan, China
EU and GDPR Compliance: All three model families (Qwen3, Llama 3.x/4, Mistral) run fully locally with zero external data transmission, ensuring GDPR compliance. Mistral (French-origin, Mistral AI) has the strongest EU compliance posture. Devstral Small 24B and Codestral 22B are French-origin (Mistral AI), Apache 2.0 — the strongest EU-origin coding models available. Both Qwen3 (Apache 2.0) and Llama 3.x/4 work equally well under EU AI Act transparency and open-source auditability requirements. Qwen3 natively supports German, French, and other EU languages without quality degradation. EU AI Act August 2026 deadline impacts classification of these model tiers.
Japan and METI Compliance: Qwen3 and Llama 3.x/4 both align with Japan's METI (Ministry of Economy, Trade and Industry) local AI governance guidelines. No special reporting required if deployed on private infrastructure within Japanese corporate networks. Qwen3 benefits from strong Japanese language support (native tokenization) among its 29 languages, making it preferred for Japanese-language workloads. Mistral is also compliant but less commonly documented in Japanese AI governance contexts. Llama 4 Scout's MoE efficiency appeals to hardware-constrained Japanese enterprises.
China and CAC Requirements: Qwen3 (Alibaba, domestic) is strongly preferred for CAC (Cyberspace Administration of China) compliance. Qwen3 is natively optimized for Chinese tokenization with no degradation across its 29-language support — a critical advantage for Mandarin and dialect support. Kimi K2.6 (Moonshot AI, MIT license) is also available for Chinese enterprise coding — top-tier performance, MIT license. Llama and Mistral are acceptable if deployed on private servers within Chinese territory, but cloud API calls incur stricter CAC scrutiny and data residency requirements. For content moderation compliance, Qwen3's Chinese training heritage ensures alignment with local content policies.
Common Mistakes When Choosing Model Families
- Comparing models at different parameter counts -- Qwen 32B vs Llama 70B is not an apples-to-apples test.
- Ignoring MoE models in family comparisons. Llama 4 Scout has 109B total parameters but only 17B active per token — it fits on 12 GB VRAM and outperforms dense 13B models. Comparing Scout's 109B total count against Qwen 3.6's 27B dense count is misleading. Compare by VRAM tier and benchmark, not parameter count.
- Using Qwen3 when Qwen3 is available. Qwen3 8B improves over Qwen3 7B on coding benchmarks. Unless you have a specific fine-tune built on Qwen3, upgrade to Qwen3.
- Not considering task-specific Mistral models. Mistral now has three distinct model lines: Small 3.1 (general), Devstral (agentic coding), Codestral (IDE autocomplete). Picking "Mistral" without specifying which model for which task wastes the family's main advantage — specialization.
- Ignoring multilingual benchmarks when choosing between models if your workload is multilingual.
- Mistral Small 3.1 overlooked: Many users skip Small 3.1 (24B) thinking it requires 30+ GB RAM. It fits at Q5 quantization with 22 GB, outperforming Llama 3.1 8B on many tasks.
Frequently Asked Questions
Is Qwen or Llama better for my use case?
For coding and multilingual tasks: Qwen 3.6 27B (77.2% SWE-bench) or Qwen3 8B. For English reasoning: Llama 3.3 70B or Llama 4 Scout for efficiency. For maximum quality per GB of RAM: Mistral Small 3.1. Test with sample prompts from your actual workload.
What is Llama 4 Scout and how is it different from Llama 3.3?
Llama 4 Scout uses Mixture-of-Experts (MoE) architecture — 17B parameters are active per token out of 109B total. This means it runs on ~10 GB VRAM (comparable to a dense 14B model) while delivering quality closer to dense 30B models. It also has a 10M token context window — the largest of any locally-runnable model. Llama 3.3 70B is a dense model requiring 40 GB VRAM. Scout offers better quality per VRAM; Llama 3.3 70B offers slightly better absolute quality if you have the hardware.
Should I use Qwen3 or Qwen3?
Use Qwen3 for new projects. Qwen3 8B improves over Qwen3 7B on coding and reasoning benchmarks. Qwen 3.6 27B (77.2% SWE-bench) is the best dense coding model available. The only reason to stay on Qwen3 is if you have an existing fine-tune or workflow that depends on its specific behavior. For fresh installations, always start with Qwen3.
How much faster is Mistral on consumer hardware?
Mistral Small 3.1 (24B) runs 1.5-2× faster than Llama 3.1 8B on the same hardware. For throughput-sensitive workloads, Mistral 7B is fastest at 40-60 tok/sec on a single GPU. Codestral 22B is optimized for FIM (fill-in-the-middle) in IDE autocomplete workflows.
Can all three run on 8 GB VRAM?
Yes, all can run 7B models at Q4 quantization on 8 GB. Qwen3 8B uses ~5 GB, Llama 3.1 8B uses ~5.5 GB, Mistral 7B uses ~4.5 GB at Q4_K_M. Llama 4 Scout (17B active, MoE) does NOT fit 8 GB — needs 12 GB.
Do I need an RTX 5090 to run these?
No. RTX 5070 (12 GB) runs 7B models comfortably and also handles Llama 4 Scout. RTX 5060 Ti (8 GB) handles all 7B variants. RTX 5090 is overkill unless running 70B models in production.
What quantization should I use?
Start with Q4_K_M (4-bit) -- good balance of quality and speed on all hardware. Use Q5_K_M if you have VRAM headroom and need higher quality. Q3_K_S for constrained devices.
Which is best for coding?
Qwen3 8B (~76% HumanEval) for 8GB tier. Qwen 3.6 27B (77.2% SWE-bench) for best dense coding. Devstral Small 24B for agentic multi-file workflows. Codestral 22B for IDE autocomplete (FIM).
Sources
- Qwen Team. (2026). Qwen3 Technical Report. -- Qwen3 family benchmarks, Qwen 3.6 27B SWE-bench (77.2%), MoE variants.
- Meta AI. (2025). Llama 4 Model Card. -- Official benchmark and architecture for Llama 4 Scout/Maverick MoE, 10M context window.
- Mistral AI. (2026). Devstral Small 24B. -- Architecture and benchmarks for agentic coding model.
- Mistral AI. (2025). Codestral. -- FIM-optimized coding model for IDE autocomplete.
- Meta AI. (2024). Llama 3.3 Model Card. -- Official benchmark data for Llama 3.3 70B (legacy, still widely used).
Update Log
- 2026-05-17: Added Legacy Benchmark Reference section comparing Mistral Small 24B, Qwen 2.5 14B, and Llama 3.1 8B. Updated title to bridge legacy and current model searches.