Qwen 3 vs Llama 3.3 vs Mistral: Local LLM Comparison 2026

Previous generation models (Qwen3, Llama 3.3) remain available on Ollama and are still widely used. This comparison focuses on current-generation models. Ready to run one? Full Qwen local setup guide →

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.

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.

Llama 4 (April 2025) introduced MoE architecture to the Llama family. Scout (17B active / 109B total, 16 experts, multimodal) offers a 10M token context window — the largest context of any locally-runnable model — but needs ~55 GB VRAM at Q4 and does not fit a 24 GB consumer GPU at normal quants (only at 1.78-bit, ~20 tok/s). Maverick (17B active / 400B total) targets multi-GPU setups. Llama 3.3 70B remains the most battle-tested dense model. For best overall on consumer hardware, Qwen 3.6 27B (fits 24 GB at Q4) outperforms Scout; choose Scout when you need its 10M context or multimodal input.

Strengths: 10M context window (Scout), multimodal input, 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: high VRAM demand (~55 GB at Q4) puts Scout out of reach for a single 24 GB consumer GPU at normal quants; 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 3.3 8B remain available and are still the most widely fine-tuned base models.

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 Small v0.3 is now outperformed on benchmarks by Qwen3 7B and Llama 3.3 8B; fewer size options at the frontier than Qwen or Llama (though specialization partially offsets this).

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 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.

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 3.3 8B (~5.5 GB) both significantly outperform Mistral Small v0.3. Qwen3 8B leads on coding; Llama 3.3 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, 16 experts) and Qwen3.6-35B-A3B (3B active / 35B total, 73.4 SWE-bench) use Mixture-of-Experts architecture — only a fraction of parameters activate per token. Scout needs ~55 GB VRAM at Q4 (it fits a 24 GB GPU only at 1.78-bit, ~20 tok/s), so it is a long-context / multimodal pick rather than a consumer-VRAM efficiency play; the smaller MoE variants are far more VRAM-friendly. gpt-oss:20b (21B total / 3.6B active MoE) also runs in 16 GB at ~o3-mini level with adjustable reasoning.

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-5.5, Claude Opus 4.8, and Gemini 3.5 — and when to choose proprietary over open-source, see how to pick the right AI model.

Many developers still run the previous generation: Mistral Small 24B (2024), Qwen 3 14B (2024), and Llama 3.3 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.

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, 1T total / 32B active MoE, Modified MIT license) is also available for Chinese enterprise coding — frontier performance (58.6 SWE-Bench Pro), Modified 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.