What is the best open source LLM in 2026?

It depends on your use case. DeepSeek R1 is best for complex reasoning and math. Llama 4 Maverick is best for multimodal (vision + text) tasks. Qwen 3 72B excels at coding. For general use, Llama 4 Maverick offers the best balance. All three are truly open source with permissive licenses allowing commercial use.

Which open source LLM is best for coding?

For coding, our top picks are: 1) Qwen 2.5 Coder 32B (best code-specific model), 2) DeepSeek Coder V2 (excellent for complex algorithms), 3) Llama 4 Maverick (best with visual code understanding). On HumanEval, Qwen Coder 32B scores 92%, followed by DeepSeek Coder at 90% and Llama 4 at 75%.

Can open source LLMs compete with GPT-4?

Yes, in many areas. DeepSeek R1 beats GPT-4 on math benchmarks (AIME 79.8% vs 9.3%). Llama 4 matches GPT-4o on MMLU (88.2%). For specific tasks like coding, reasoning, or multilingual text, open models are now competitive. GPT-4 still leads on some creative and instruction-following tasks, but the gap has narrowed significantly.

What hardware do I need to run the best open source LLMs?

Minimum specs for running top open models: 8GB VRAM for 7B-8B models (Llama 3.1 8B, Qwen 7B), 16GB for 14B-34B (Scout, Qwen 32B), 24GB for 70B models (Maverick, DeepSeek R1 70B distilled). An RTX 4090 (24GB) handles most top models well. For the absolute best performance, the RTX 5090 (32GB) gives more headroom.

Are open source LLMs really free for commercial use?

Most are, but licenses vary. Llama 4 uses the Llama Community License (free for <700M MAU). DeepSeek uses MIT license (fully permissive). Qwen uses Apache 2.0 (fully permissive). Mistral models use Apache 2.0. Always check the specific license, but for most businesses, these models are completely free to deploy.

What is the difference between DeepSeek R1 and V3?

DeepSeek V3 is a general-purpose 671B MoE model for everyday tasks. DeepSeek R1 is specialized for reasoning with chain-of-thought capabilities—it shows its thinking process and excels at math, logic, and complex problems. V3 is faster for simple queries; R1 is better when you need step-by-step problem solving.

What is the best open source LLM for limited VRAM (8-16GB)?

For 8GB VRAM: Phi-4 14B Q4 (best quality), Llama 3.1 8B (fastest), Qwen 2.5 7B (good multilingual). For 16GB VRAM: Llama 4 Scout Q4 (near-70B quality at 8B speed), Qwen 2.5 14B (excellent all-around), Mistral 12B (fast instruction following). Scout is the breakthrough here—MoE architecture means 109B total params but only 17B active, fitting in 12GB with excellent performance.

How do open source LLMs compare to Claude and GPT-4?

Open source is now competitive: DeepSeek R1 beats GPT-4 on math (79.8% vs 9.3% AIME). Llama 4 matches GPT-4o on MMLU (88.2%). Claude 3.5 still leads on coding (92% HumanEval) but Qwen Coder is close (92%). GPT-4 and Claude maintain edges in: instruction following, creative writing, and "vibes" (subjective quality). For technical tasks, open source is at parity or better.

What is the best open source LLM for enterprise deployment?

For enterprise: Qwen 3 72B (Apache 2.0, no restrictions, strong multilingual), Mistral Large 2 (European company, GDPR-friendly), or Llama 4 Maverick (permissive for <700M MAU). Key considerations: license terms, support availability, model stability, and deployment tooling. Qwen and Mistral offer the most permissive licenses. Consider vLLM or TGI for production serving infrastructure.

How often are new open source LLMs released?

Major releases happen every 2-4 months from each lab. Meta (Llama): annual major versions with quarterly updates. DeepSeek: 2-3 major releases per year. Alibaba (Qwen): quarterly releases. Mistral: 2-3 per year. The pace accelerated dramatically in 2025-2026. Follow our newsletter or Hugging Face's model hub for announcements. Most releases include multiple size variants (7B, 14B, 32B, 70B+).

Can I fine-tune open source LLMs for my specific use case?

Yes, all top open source LLMs support fine-tuning. QLoRA enables fine-tuning 70B models on a single 24GB GPU. Tools: Unsloth (fastest, 2x speed), Axolotl (most features), HuggingFace TRL (easiest). Typical requirements: 1000-10000 examples, 1-4 hours training time, 16-24GB VRAM. Fine-tuned models can dramatically outperform base models on specific tasks while maintaining general capabilities.

What open source LLMs support function calling/tool use?

Models with native function calling: Llama 4 (all variants), Llama 3.1 (8B+), Qwen 2.5 (all sizes), Mistral (7B+), DeepSeek V3. These models can output structured JSON for tool invocation. For agents, Llama 4 and Qwen 2.5 have the most reliable tool use. Hermes fine-tunes add function calling to models that lack it. Most modern models (2024+) support some form of structured output.

Best Open Source LLMs 2026: DeepSeek R1 vs Llama 4 vs Qwen 3

Q: What is the difference between DeepSeek R1 and V3?

DeepSeek V3 is a general-purpose 671B MoE model for everyday tasks. DeepSeek R1 is specialized for reasoning with chain-of-thought capabilities—it shows its thinking process and excels at math, logic, and complex problems. V3 is faster for simple queries; R1 is better when you need step-by-step problem solving.

Q: What is the best open source LLM for limited VRAM (8-16GB)?

For 8GB VRAM: Phi-4 14B Q4 (best quality), Llama 3.1 8B (fastest), Qwen 2.5 7B (good multilingual). For 16GB VRAM: Llama 4 Scout Q4 (near-70B quality at 8B speed), Qwen 2.5 14B (excellent all-around), Mistral 12B (fast instruction following). Scout is the breakthrough here—MoE architecture means 109B total params but only 17B active, fitting in 12GB with excellent performance.

Q: How do open source LLMs compare to Claude and GPT-4?

Open source is now competitive: DeepSeek R1 beats GPT-4 on math (79.8% vs 9.3% AIME). Llama 4 matches GPT-4o on MMLU (88.2%). Claude 3.5 still leads on coding (92% HumanEval) but Qwen Coder is close (92%). GPT-4 and Claude maintain edges in: instruction following, creative writing, and "vibes" (subjective quality). For technical tasks, open source is at parity or better.

Q: What is the best open source LLM for enterprise deployment?

For enterprise: Qwen 3 72B (Apache 2.0, no restrictions, strong multilingual), Mistral Large 2 (European company, GDPR-friendly), or Llama 4 Maverick (permissive for <700M MAU). Key considerations: license terms, support availability, model stability, and deployment tooling. Qwen and Mistral offer the most permissive licenses. Consider vLLM or TGI for production serving infrastructure.

Q: How often are new open source LLMs released?

Major releases happen every 2-4 months from each lab. Meta (Llama): annual major versions with quarterly updates. DeepSeek: 2-3 major releases per year. Alibaba (Qwen): quarterly releases. Mistral: 2-3 per year. The pace accelerated dramatically in 2025-2026. Follow our newsletter or Hugging Face's model hub for announcements. Most releases include multiple size variants (7B, 14B, 32B, 70B+).

2026 Open Source LLM Rankings

🏆

Best Reasoning

DeepSeek R1

79.8% AIME, visible thinking

👁️

Best Multimodal

Llama 4 Maverick

Vision + text, 10M context

💻

Best Coding

Qwen 2.5 Coder 32B

92% HumanEval, multi-lang

The State of Open Source AI in 2026

2025-2026 marked a turning point. Open source models now match or exceed closed models on most benchmarks:

Benchmark	Best Open Model	Score	GPT-4o Score
AIME 2024 (Math)	DeepSeek R1	79.8%	9.3%
MMLU (Knowledge)	Llama 4 Maverick	88.2%	88.7%
HumanEval (Code)	Qwen 2.5 Coder	92%	90.2%
GPQA (Science)	DeepSeek R1	71.5%	49.9%

Top 10 Open Source LLMs of 2026

1. DeepSeek R1 - Best for Reasoning

Why it's #1 for reasoning: Chain-of-thought with visible "thinking" tokens, MIT licensed, and beats GPT-4 on math by 8x.

Metric	Value
Architecture	671B MoE (37B active)
VRAM (Q4)	24GB (70B distilled)
License	MIT
Best For	Math, logic, complex problems

ollama run deepseek-r1:32b

2. Llama 4 Maverick - Best for Multimodal

Why it's #1 for multimodal: Native vision + text, 10M token context, MoE efficiency.

Metric	Value
Architecture	400B MoE (17B active)
VRAM (Q4)	24GB
License	Llama Community
Best For	Vision tasks, general use

ollama run llama4-maverick

3. Qwen 2.5 Coder 32B - Best for Coding

Why it's #1 for coding: 92% HumanEval, extensive language support, code completion optimized.

Metric	Value
Architecture	32B Dense
VRAM (Q4)	20GB
License	Apache 2.0
Best For	Code generation, debugging

ollama run qwen2.5-coder:32b

4. DeepSeek V3 - Best Value MoE

Why it ranks here: 671B parameters with only 37B active, excellent all-around performance.

Metric	Value
Architecture	671B MoE (37B active)
VRAM (Q4)	24GB
License	MIT
Best For	General tasks, API replacement

5. Qwen 3 72B - Best Large Dense Model

Why it ranks here: Strongest dense model, excellent multilingual, Apache licensed.

Metric	Value
Architecture	72B Dense
VRAM (Q4)	44GB
License	Apache 2.0
Best For	Enterprise, multilingual

6. Llama 4 Scout - Best Efficient Model

Why it ranks here: Near-Llama-3.1-70B quality at 8B-model speeds.

Metric	Value
Architecture	109B MoE (17B active)
VRAM (Q4)	12GB
License	Llama Community
Best For	Fast inference, edge devices

7. Mistral Large 2 - Best European Model

Why it ranks here: Strong instruction following, good for enterprise.

Metric	Value
Architecture	123B Dense
VRAM (Q4)	48GB
License	Apache 2.0
Best For	Enterprise, European compliance

8. Gemma 3 27B - Best Small-Medium Model

Why it ranks here: Google's best open model, excellent efficiency.

Metric	Value
Architecture	27B Dense
VRAM (Q4)	18GB
License	Gemma Terms
Best For	Balanced performance

9. Yi-1.5 34B - Best Chinese Alternative

Why it ranks here: Strong bilingual (EN/ZH), competitive benchmarks.

Metric	Value
Architecture	34B Dense
VRAM (Q4)	22GB
License	Apache 2.0
Best For	Chinese language tasks

10. Phi-4 14B - Best Ultra-Efficient

Why it ranks here: Microsoft's small model punches way above its weight.

Metric	Value
Architecture	14B Dense
VRAM (Q4)	10GB
License	MIT
Best For	Edge, mobile, constrained resources

Comparison by Use Case

For General Chat

Model	Quality	Speed	VRAM
Llama 4 Maverick	Excellent	Fast	24GB
DeepSeek V3	Excellent	Fast	24GB
Qwen 3 72B	Excellent	Medium	44GB

Winner: Llama 4 Maverick (multimodal adds value)

For Coding

Model	HumanEval	Speed	VRAM
Qwen 2.5 Coder 32B	92%	Fast	20GB
DeepSeek Coder V2	90%	Fast	24GB
Llama 4 Maverick	75%	Medium	24GB

Winner: Qwen 2.5 Coder 32B

For Math/Reasoning

Model	AIME	MATH	VRAM
DeepSeek R1	79.8%	97.3%	24GB
Qwen 3 72B	52.4%	83.1%	44GB
Llama 4 Maverick	45.2%	78.3%	24GB

Winner: DeepSeek R1 (by a huge margin)

For 8GB VRAM

Model	Quality	Speed
Llama 3.1 8B	Good	55 tok/s
Qwen 2.5 7B	Good	60 tok/s
Phi-4 14B Q4	Very Good	40 tok/s

Winner: Phi-4 14B (best quality at this VRAM)

How to Choose

Need reasoning/math?     → DeepSeek R1
Need vision/multimodal?  → Llama 4 Maverick
Need coding?             → Qwen 2.5 Coder 32B
Need speed?              → Llama 4 Scout
Limited VRAM (8GB)?      → Phi-4 14B or Llama 3.1 8B
Enterprise deployment?   → Qwen 3 72B or Mistral Large

Key Takeaways

DeepSeek R1 dominates reasoning with unprecedented math scores
Llama 4 brings multimodal to open source at GPT-4V quality
Qwen leads coding with 92% HumanEval
MoE architecture is the trend - better quality per VRAM
24GB VRAM runs most top models well
All top models are commercially usable under permissive licenses

Next Steps

Set up DeepSeek R1 for reasoning tasks
Install Llama 4 for multimodal
Choose your GPU for local inference
Build AI agents with these models

The open source AI ecosystem has matured. For most use cases, you no longer need to pay for cloud APIs—the best models run free on your own hardware.

Best Open Source LLMs 2026: DeepSeek R1 vs Llama 4 vs Qwen 3

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2026 Open Source LLM Rankings

The State of Open Source AI in 2026

Top 10 Open Source LLMs of 2026

1. DeepSeek R1 - Best for Reasoning

2. Llama 4 Maverick - Best for Multimodal

3. Qwen 2.5 Coder 32B - Best for Coding

4. DeepSeek V3 - Best Value MoE

5. Qwen 3 72B - Best Large Dense Model

6. Llama 4 Scout - Best Efficient Model

7. Mistral Large 2 - Best European Model

8. Gemma 3 27B - Best Small-Medium Model

9. Yi-1.5 34B - Best Chinese Alternative

10. Phi-4 14B - Best Ultra-Efficient

Comparison by Use Case

For General Chat

For Coding

For Math/Reasoning

For 8GB VRAM

How to Choose

Key Takeaways

Next Steps

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Written by Pattanaik Ramswarup