Local LLMs in 2026 — Llama vs Qwen vs Mistral vs DeepSeek compared

Four families still dominate open-source LLMs in 2026: Meta’s Llama, Alibaba’s Qwen, Mistral’s models, and DeepSeek. Each has moved to Mixture-of-Experts (MoE) architectures at the top end. This guide compares them across the tasks developers actually care about — with practical smaller-model recommendations for consumer hardware.

The 30-second answer

• Coding: Qwen 3 Coder or DeepSeek Coder V2 (or bigger DeepSeek V4 variants if you have the VRAM)
• General chat / writing: Llama 4 or Qwen 3.5
• Reasoning / math: DeepSeek V4 / V4 Pro
• Multilingual: Qwen 3.x (strongest non-English support)
• Long context: Llama 4 Scout
• Speed on modest hardware: Llama 3.2 3B, Qwen 3 7B, or Mistral Small variants

Model families overview

Llama (Meta)

• Latest: Llama 4 Scout, Llama 4 Maverick (400B total / 17B active MoE); Llama 3.x line still supported for smaller sizes (1B, 3B, 8B)
• Strengths: Balanced general performance, largest enterprise deployment base, strong ecosystem, fine-tune community, Llama 4 Scout excels at long context
• Weaknesses: English-heavy training; the flagship MoE requires serious VRAM

Qwen (Alibaba)

• Latest open-weight: Qwen 3, Qwen 3.5 (397B / 17B MoE), Qwen 3.6; Qwen 3 Coder (specialized, only 30B and 480B sizes); Qwen-VL multimodal variants. Note: Qwen 3.7 (Max/Plus) shipped API-only with no open weights and can’t be run locally.
• Strengths: Excellent coding, strong reasoning, best multilingual, small dense models still competitive on consumer GPUs
• Weaknesses: Slightly less community/tooling than Llama outside China

Mistral

• Latest: Mistral Large 3 (675B total / 41B active MoE, 256K context, 80+ languages, Apache 2.0); Mistral Small 4 (119B / 6B, March 2026)
• Strengths: Fast inference for active-param count, permissive Apache 2.0 license across the board, strong multilingual
• Weaknesses: Flagship needs enterprise-grade VRAM; smaller models are the practical local pick

DeepSeek

• Latest: DeepSeek V3.2, DeepSeek V4, DeepSeek V4 Pro (1.6T total / 49B active MoE); DeepSeek Coder V2 for local use
• Strengths: State-of-the-art on math and reasoning; competitive coding; MIT license
• Weaknesses: Flagship sizes require significant VRAM even in MoE form

Also worth knowing

• GLM-5 / GLM-5.1 — Zhipu AI’s flagship, competitive on reasoning
• Gemma 4 — Google’s open family, Apache 2.0, tuned for on-device use
• Kimi — Moonshot AI, long-context specialist

Comparison — recommended model per VRAM tier

Important: the 2026 flagship models (Llama 4 Maverick, DeepSeek V4 Pro, Mistral Large 3) are MoE giants and don’t fit on consumer GPUs at all. The practical local picks remain the smaller dense models from each family — mostly the previous-gen 7B-32B lineup, which is still very good.

8GB VRAM (RTX 3060 8GB, laptop GPUs)

Best pick: Qwen 3 8B (5.2GB at Q4_K_M) or Qwen 2.5 Coder 7B for code (no Qwen 3 Coder exists in a size that fits 8GB — smallest Qwen 3 Coder is 30B)

ollama pull qwen3:8b
ollama pull qwen2.5-coder:7b

• Runs comfortably at Q4_K_M quantization
• 25-40 tokens/sec on 8GB
• Competitive with Llama 3.1 8B and Mistral 7B on most tasks

Alternatives:

• Llama 3.2 3B — faster (60+ tokens/sec) but less capable
• Mistral 7B — comparable to Qwen 8B, slightly less strong on code
• Gemma 4 (small variant) — very fast, tuned for on-device

12GB VRAM (RTX 3060 12GB, RTX 4070)

Best pick: Qwen 3 14B or DeepSeek Coder V2 16B

ollama pull qwen3:14b
ollama pull deepseek-coder-v2:16b

• 14-15 tokens/sec
• Big quality jump from 8B models
• Coding tasks reach solid mid-tier cloud quality

16GB VRAM (RTX 4080 laptop, RTX 4070 Ti)

Best pick: Qwen 3 14B (fits comfortably) or Llama 3.1 8B at higher quant

ollama pull qwen3:14b
ollama pull llama3.1:8b-instruct-q8_0

• 12-18 tokens/sec depending on model
• Note: Qwen3:32b needs ~20GB at Q4_K_M — doesn’t fit 16GB. Mixtral 8x7B needs 26GB even at Q4. Stay at 14B for 16GB cards.

24GB VRAM (RTX 3090, RTX 4090)

Best pick: Qwen 3 32B (Q4_K_M ~20GB fits with room for KV cache) or DeepSeek Coder V2 33B

ollama pull qwen3:32b

• 10-15 tokens/sec
• Approaches cloud model quality on most tasks

32GB+ VRAM / multi-GPU / Apple Silicon 64GB+

Best pick: Llama 3.3 70B at Q4_K_M (~40GB) or DeepSeek V3.2 with partial offload

ollama pull llama3.3:70b

• Note on Llama 4 Scout (109B total / 17B active MoE): all 109B params must be resident in memory even though only 17B are “active” per token — needs ~55-60GB at Q4, so it does NOT fit 24GB. It’s a 32GB+ or Mac Studio model.
• 5-10 tokens/sec on 70B
• Near-frontier quality on most tasks

Task-specific recommendations

For code completion / autocomplete

Winner: Qwen 3 Coder (any size)

Specifically trained on code across many languages. Beats general models of same size on programming tasks. Feed it your codebase context and it maintains style well.

For code review / refactoring

Winner: DeepSeek Coder V2 16B+ or Qwen 3 Coder 14B+

Larger context understanding matters here. Both excel at explaining what code does and suggesting improvements.

For writing (blog posts, docs, emails)

Winner: Llama 3.3 70B / Llama 4 Scout if you have VRAM, else Qwen 3 14B

Prose quality matters. Llama’s larger sizes produce more natural writing. Qwen 14B is close and runs on cheaper hardware.

For reasoning / math / logic

Winner: DeepSeek V4 or V4 Pro (if you can run it)

Chain-of-thought reasoning at inference time gives significant gains on math benchmarks (GSM8K, MATH). At smaller sizes, Qwen 3 tends to reason more carefully than same-size Llama.

For non-English languages

Winner: Qwen 3.x (any size)

Trained heavily on Chinese but strong across many languages including Hindi, Arabic, Spanish. Better than Llama for multilingual tasks. Mistral Large 3 is also a strong 80+ language option if you have the VRAM.

For structured data extraction / JSON

Winner: Mistral 7B / Small 4 or Qwen 3 7B

Both follow instructions to output structured data more reliably than Llama at similar sizes.

What quality equivalence looks like in 2026

The frontier gap has narrowed a lot. Rough equivalences (subjective — verify on your prompts):

For most developer tasks (coding, docs, analysis), a well-quantized 14B+ model on decent hardware is genuinely useful. For everything else, cloud APIs remain necessary.

Cost comparison

Local (one-time hardware):

• RTX 3060 12GB: ~₹28,000-32,000
• RTX 4070: ~₹55,000-62,000
• RTX 5090 (released Jan 2025): significantly more; consider previous generations for value
• Power cost: ~₹500/month for 4 hours/day inference (varies by GPU)

API (per month at moderate use, 2026 pricing):

• Claude Haiku 4.5: often under $10 at hobbyist volume
• Claude Sonnet 5: $10-50
• GPT-5.6-luna: often under $10
• GPT-5.6-sol: $10-50
• Groq (open-source hosted): $5-30

For high-volume use, local pays back in 6-18 months. For occasional use, APIs are cheaper.

Reproduce this yourself

Free hosted access to open-source models:

• Groq (https://groq.com) — free tier, extremely fast Llama and Mistral hosting
• Together.ai (https://together.ai) — hosted open-source models, free credits
• Hugging Face Chat (https://huggingface.co/chat) — try many models in browser
• Google Colab — free GPU to install Ollama and test locally-styled workflows

Try the same coding prompt in 3 different models. Compare quality subjectively for your specific tasks.

The 2026 landscape

Open-source has fully caught up on many tasks. The capability gap on top benchmarks is now single-digit points, not generations. Almost every flagship open model in 2026 is a sparse MoE:

• DeepSeek V4 Pro: 1.6T total / 49B active
• Mistral Large 3: 675B / 41B
• Llama 4 Maverick: 400B / 17B
• Qwen 3.5: 397B / 17B

MoE architectures give closed-model-competitive quality at inference costs closer to a dense model of the active-parameter size — but the total-parameter storage still demands enterprise-grade infrastructure to serve. For most solo developers, the practical local models are still in the 7B-32B dense range.

Bottom line

• On 8GB VRAM: Qwen 3 7B (Coder variant for coding). Best all-around.
• On 12GB: Qwen 3 14B or DeepSeek Coder 16B.
• On 24GB: Llama 3.3 70B (Q4, ~40GB with offload) or Qwen 3 32B (Q4 fits fully) — near-frontier locally. Llama 4 Scout needs 32GB+ despite being MoE (all 109B params must be resident).
• For reasoning tasks specifically: DeepSeek V4 at whatever size fits, or DeepSeek Coder V2 for local.
• For any budget below 8GB: use APIs. Local isn’t practical yet at that tier.

Model versions in this space move every few months. Re-verify with ollama list and each family’s model page before committing to one for a long-lived setup.