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Open Source LLM Models: The Definitive List

Deploybase · February 19, 2026 · LLM Guides

Contents

Open Source LLM Models: Overview

Open-source LLM models have become production-viable alternatives to proprietary APIs. As of March 2026, the market includes frontier models (Llama 4 Scout/Maverick MoE, DeepSeek V3/R1, Qwen 2.5 Max), capable mid-range options (Mistral, Llama 3.1 70B), and efficient edge models (Phi-3.5, Gemma 2 9B). This definitive guide ranks models by capability, examines deployment economics, compares with API costs, and identifies the right choice for different workloads.

The decision between open-source and proprietary APIs depends on scale, privacy requirements, infrastructure expertise, and total cost. Small teams with occasional API usage find proprietary APIs cheaper. Large teams running millions of inferences daily find open-source self-hosting more economical.

Tier 1: Frontier Open-Source Models

Llama 4: Meta's Frontier MoE Models

Llama 4 is Meta's 2026 frontier model family using mixture-of-experts (MoE) architecture. Two main variants: Scout (17B active / 109B total, 10M token context) and Maverick (17B active / 400B total, 1M token context). Both models activate 17B parameters per token, enabling efficient inference despite large total parameter pools.

Performance is competitive with proprietary models. Maverick on MMLU (knowledge): 91%+ accuracy. On coding (HumanEval): 89%+. Scout achieves slightly lower benchmark scores but with far better latency and context length.

Strengths: Strong across all domains (reasoning, coding, knowledge work), excellent instruction-following, multimodal vision capabilities on both Scout and Maverick. Scout's 10M token context window is unmatched among open-source models.

Weaknesses: Scout requires a single H100 (quantized); Maverick requires 8x H100 or equivalent. Consumer GPU deployment not practical.

Licensing: Llama 4 Community License. Commercial use is permitted with restrictions. Fine-tuning is allowed. Use in closed models is restricted.

Deployment Cost (March 2026):

  • Scout self-host on RunPod H100 (quantized): $1.99/hour = $47.76/day
  • Maverick self-host on RunPod 8x H100: $49.24/hour = $1,181.76/day
  • Inference API (Together AI): Scout $0.11/$0.34, Maverick $0.19/$0.85 per million tokens

For teams running 1 million tokens daily (~$0.03-0.05 cost), self-hosting makes economic sense. Below 100K tokens daily, API inference services are cheaper.

DeepSeek V3: Chinese Frontier with Strong Performance

DeepSeek V3 (released December 2025) is a 671B mixture-of-experts model from Chinese startup DeepSeek. Architecture uses conditional compute (not all parameters activate per token), reducing computational demands.

Performance rivals Llama 4 on most benchmarks:

  • MMLU: 92%+ accuracy
  • GSM8K: 90%+ accuracy
  • HumanEval: 87%+ (superior to Llama on coding)
  • MATH: 57%+ (best open-source)

Strengths: Exceptional mathematical reasoning, strong coding, efficient architecture (mixture-of-experts uses less compute than dense 405B), multilingual support (including Chinese, better than English-focused Llama).

Weaknesses: Less English-centric training data may slightly reduce English performance. Mixture-of-experts requires compatible infrastructure. Community support smaller than Llama (less documentation, fewer tools).

Licensing: MIT License (most permissive). Commercial use, fine-tuning, and closed-source derivatives are all allowed without restrictions.

Deployment Cost:

  • Self-host on RunPod H100 (single): $1.99/hour with quantization = $47.76/day
  • Inference service: $0.02-0.04 per 1000 tokens (cheaper than Llama due to efficiency)

DeepSeek's efficiency makes it economical for self-hosting. For mathematical workloads and cost-sensitive deployments, DeepSeek is superior to Llama 4.

DeepSeek R1: Reasoning-Focused Variant

DeepSeek R1 is a reasoning-specialized variant. The model allocates extra compute to chain-of-thought reasoning before generating responses. Similar in concept to OpenAI's o1 or Anthropic's extended thinking modes.

Performance on reasoning benchmarks:

  • MATH: 61%+ accuracy (competes with GPT-5 Pro)
  • Scientific reasoning: exceptional
  • Coding problem-solving: 88%+

Strengths: Best open-source for complex reasoning, exceptional mathematical proofs, superior to V3 on novel problem-solving.

Weaknesses: Slower (30-60 second latencies for complex problems). Higher token consumption due to reasoning overhead. Not suitable for real-time applications.

Deployment Cost:

  • Self-host on RunPod H100: $1.99/hour = $47.76/day
  • Cost per reasoning task: $0.30-0.60 (due to token overhead, 5-10x higher than standard inference)

For research, academic problems, and novel problem-solving, R1 is the best open-source choice. For routine inference, V3 is more economical.

Qwen 2.5 Max: Alibaba's Frontier Contender

Alibaba's Qwen 2.5 Max (2025) is a 405B parameter dense model matching Llama 4 in scale. Architecture includes Grouped Query Attention for efficiency and 128K token context.

Performance:

  • MMLU: 91%+ accuracy
  • Coding: 84%+ accuracy
  • Chinese understanding: best-in-class (stronger than Llama on non-English)

Strengths: Excellent for multilingual applications, competitive reasoning, good instruction-following.

Weaknesses: Requires as much compute as Llama 4 despite similar performance. Community ecosystem smaller than Llama. Documentation primarily in Chinese.

Licensing: Alibaba's proprietary license. Commercial use requires explicit permission. Fine-tuning restrictions apply.

Deployment Cost: Identical to Llama 4 (same parameter count, architecture).

Qwen is preferred for multilingual applications. For English-only deployments, Llama 4 has better community support.

Tier 2: Mid-Range High-Quality Models

Llama 3.1 70B: Proven Workhorse

Llama 3.1 70B (released April 2024, refined through 2025) remains the most deployed open-source model. While older than frontier models, it offers proven stability and community support.

Performance:

  • MMLU: 88%+ accuracy
  • Coding: 80%+ accuracy
  • Instruction-following: exceptional

Strengths: Minimal hallucination, excellent instruction-following, reliable across diverse tasks, extensive community tools and integrations.

Weaknesses: Smaller than frontier models (70B vs 405B parameters). Performance on frontier benchmarks lags latest models.

Licensing: Open Community License (same as Llama 4).

Deployment Cost:

  • Self-host on RunPod A100: $1.19/hour = $28.56/day
  • Inference service: $0.01-0.015 per 1000 tokens

Llama 3.1 70B is economical and production-ready. For teams not requiring frontier performance, this is the best value. Cost per task is 50-70% lower than 405B variants.

Mistral Large: Efficiency-Focused 123B

Mistral Large (released December 2024) is an efficient 123B parameter model. Architecture is standard transformer with emphasis on inference speed and context (128K tokens).

Performance:

  • MMLU: 87% accuracy
  • Coding: 82% accuracy
  • Speed: 2x faster than equivalently-sized models

Strengths: Fast inference, efficient memory usage (requires 80GB VRAM vs 160GB for equivalent dense models), 128K context enables long documents.

Weaknesses: Performance slightly trails Llama 3.1 70B on reasoning. Community smaller than Llama.

Licensing: Mistral Research License (allows commercial use, modifications for research, but derivatives must remain open-source).

Deployment Cost:

  • Self-host on RunPod A100: $1.19/hour = $28.56/day
  • Speed advantage: 2x throughput vs Llama 3.1, reducing GPU hours needed by 50%
  • Effective cost: equivalent to Llama 3.1 70B but with better latency

Mistral Large is preferred when inference speed matters (real-time systems, interactive applications).

Phi-3.5 14B: Quality per Parameter

Phi (Microsoft) series emphasizes quality rather than parameter count. Phi-3.5 is 14B parameters, specifically optimized for reasoning and coding.

Performance:

  • MMLU: 85% accuracy
  • Coding: 79% accuracy
  • Mathematical reasoning: 75% (exceptional for size)
  • Speed: 3x faster than 70B models on CPU

Strengths: Smallest model in this tier yet competitive performance. Excellent for devices (edge inference, mobile, CPU-only). Minimal compute requirements.

Weaknesses: Smaller parameter count means less knowledge capacity. Not ideal for general knowledge work.

Licensing: MIT License (permissive).

Deployment Cost:

  • CPU inference (no GPU): $0/hour (on-device)
  • GPU acceleration (RunPod A100): $1.19/hour
  • Inference service: $0.003-0.005 per 1000 tokens

Phi-3.5 is optimal for edge deployment and cost minimization. For reasoning tasks where knowledge capacity is less critical, Phi matches or exceeds larger models while costing far less.

Tier 3: Lightweight and Edge-Optimized

Gemma 2 9B: Google's Efficient Open Model

Gemma 2 9B (released June 2024) is Google's small-scale open model designed for efficiency. Despite 9B parameters, performance remains strong.

Performance:

  • MMLU: 81% accuracy
  • Coding: 70% accuracy
  • Speed: 5-10x faster than 70B models

Strengths: Runs on consumer GPUs (requires <10GB VRAM), CPU inference is feasible, Google backing ensures quality, integrated into Ollama and other tools.

Weaknesses: Knowledge is limited. General knowledge queries sometimes yield incomplete answers. Reasoning is weak on novel problems.

Licensing: Gemma License (allows commercial use, redistribution, fine-tuning).

Deployment Cost:

  • CPU inference: $0
  • GPU acceleration (A100): $1.19/hour
  • Inference service: $0.002-0.003 per 1000 tokens

Gemma 2 9B is ideal for chatbots, content moderation, and classification. For knowledge-intensive work, larger models are necessary.

Falcon 7B: Lightweight Generalist

Falcon 7B (Technology Innovation Institute, 2023) is a 7B parameter model trained on 1.5T tokens. While older, it's production-tested and stable.

Performance:

  • MMLU: 78% accuracy
  • Coding: 65% accuracy

Strengths: Very small, runs on consumer hardware easily, proven reliability, extensive community fine-tuning examples.

Weaknesses: Older architecture, noticeable performance gap compared to newer models, less capable on complex reasoning.

Licensing: Apache 2.0 (permissive).

Deployment Cost: CPU-feasible, minimal infrastructure required.

Falcon 7B is deprecated in favor of newer models. For new projects, prefer Phi-3.5 or Gemma 2 instead.

BLOOM 176B: Community Multilingual

BLOOM (BigScience, 2022) is 176B parameters trained on 46 languages. While large, it's multilingual-first and community-driven.

Performance:

  • Multilingual MMLU: competitive
  • English performance: slightly below Llama

Strengths: Multilingual support, permissive license (Open RAIL License), trained with community input and ethical guidelines.

Weaknesses: Outdated architecture, performance lags modern models, requires high compute (larger than necessary).

Licensing: Open RAIL License (allows commercial use with ethical use clause).

Newer models like Qwen 2.5 or Llama with multilingual fine-tuning have surpassed BLOOM. BLOOM is historical interest rather than recommendation for new deployments.

Parameter Counts and Architecture

Model sizes vary substantially, affecting memory and compute requirements.

ModelParametersArchitectureContextMemory (FP16)
DeepSeek V3671B (MoE)Mixture-of-Experts256K160GB (active)
Llama 4 Scout17B active / 109B total (MoE)Mixture-of-Experts10M~218GB (FP16)
Llama 4 Maverick17B active / 400B total (MoE)Mixture-of-Experts1M~800GB (FP16)
Qwen 2.5 Max405BDense128K800GB
Llama 3.1 70B70BDense128K140GB
Mistral Large123BDense128K160GB
DeepSeek R1671B (dense)Standard Transformer128K1,342GB (FP16)
Phi-3.5 14B14BDense128K28GB
Gemma 2 9B9BDense8K18GB
Falcon 7B7BDense8K14GB

Memory requirements show dramatic differences. A single H100 (80GB) handles Phi-3.5 easily, Llama 3.1 70B with quantization, Llama 4 Scout with INT4 quantization (~55GB), or DeepSeek V3 with quantization. Llama 4 Maverick and DeepSeek R1 require multi-GPU clusters even when quantized.

Licensing and Restrictions

Licensing affects deployment legality and restrictions.

Most Permissive: MIT (DeepSeek V3, Phi-3.5), Apache 2.0 (Falcon). Allow commercial use, modifications, redistribution without restriction.

Community Licenses: Llama (OComL), Mistral (Research License). Allow commercial use but restrict closed-source derivatives. Fine-tuning is permitted.

Restrictive: Alibaba Qwen (proprietary), some Gemma variants. Require explicit permission for commercial use or have derivative restrictions.

For production deployments, MIT and Apache 2.0 licensed models are safest. Community licenses are fine if developers intend to keep modifications open-source.

Deployment Costs

Cost analysis reveals the open-source vs API tradeoff.

Scenario 1: High-Volume Inference (10M tokens daily)

Tokens per query: 3,000 input + 500 output = 3,500 tokens Daily queries: ~2,860

Proprietary APIs:

  • Claude Sonnet 4.6: $3/$15 = $102/day
  • GPT-4.1: $2/$8 = $48/day
  • Gemini 2.5 Flash: $0.30/$2.50 = $6.15/day

Open-Source Self-Hosted:

  • Llama 3.1 70B on RunPod A100: $28.56/day + 10% overhead = $31.42/day (60% cheaper than Sonnet)
  • Llama 3.1 70B with quantization on cheaper hardware: $15-20/day

For massive volumes, self-hosting is significantly cheaper.

Scenario 2: Medium Volume (1M tokens daily)

286 queries daily

Proprietary APIs:

  • Gemini 2.5 Flash: $0.62/day
  • GPT-4.1: $4.80/day
  • Claude Sonnet 4.6: $10.20/day

Open-Source Self-Hosted:

  • Llama 3.1 70B: $28.56/day (minimum)
  • Inference service (Replicate, Modal): $0.01 per 1000 tokens = $10/day

At medium volume, API services are competitive with self-hosting. Inference services (Replicate) match proprietary API cost while offering open-source models.

Scenario 3: Low Volume (100K tokens daily)

28 queries daily

Proprietary APIs:

  • Gemini 2.5 Flash: $0.06/day
  • GPT-4.1: $0.48/day
  • Claude: $1.02/day

Open-Source:

  • Inference service: $1/day
  • Self-hosting: $28.56/day minimum

Proprietary APIs win. The infrastructure cost of self-hosting (GPU rental minimum) exceeds the cost of low-volume API calls.

Breakeven Analysis:

  • Self-hosting makes sense at 5M+ tokens daily
  • Below 5M, inference services or proprietary APIs are cheaper
  • Between 1M-5M, service selection (inference cost, API cost) determines winner

Benchmarks and Performance

Standardized benchmarks enable comparison.

MMLU (General Knowledge)

Frontier: Llama 4 Maverick 91%, DeepSeek V3 92%, Qwen 2.5 Max 91% Mid-range: Llama 3.1 70B 88%, Mistral Large 87% Lightweight: Phi-3.5 85%, Gemma 2 9B 81%, Falcon 7B 78%

Performance scales with parameter count, but architecture matters. Phi-3.5 14B matches Llama 3.1 70B on MMLU despite 5x parameter difference.

HumanEval (Coding)

Frontier: DeepSeek V3 87%, Llama 4 Maverick 89%, Qwen 2.5 85% Mid-range: Mistral Large 82%, Llama 3.1 70B 80% Lightweight: Phi-3.5 79%, Gemma 2 9B 70%, Falcon 7B 65%

DeepSeek V3 slightly leads on coding, likely due to specialized training.

MATH (Mathematical Reasoning)

Frontier: DeepSeek R1 61%, DeepSeek V3 57%, Llama 4 Maverick 55% Mid-range: Llama 3.1 70B 47%, Mistral Large 45% Lightweight: Phi-3.5 42%

This benchmark shows where reasoning matters. Frontier models significantly outperform mid-range. DeepSeek R1's reasoning-focused design shows here.

FAQ

Q: Can I run Llama 4 locally on my computer?

A: Not practically. Llama 4 405B requires 800GB of memory. Consumer hardware maxes at 96GB. You need GPU cloud infrastructure (RunPod, CoreWeave) or inference services (Replicate, Baseten).

Llama 3.1 70B with 4-bit quantization fits on 40GB GPUs (RTX 6000, A6000). This is more accessible but still requires dedicated GPU hardware.

Phi-3.5 14B runs on consumer GPUs (<10GB) or even CPU. This is practically local-deployable.

Q: Should I self-host or use inference services?

A: For volumes under 5M tokens daily, use inference services (Replicate, Baseten, etc.) or proprietary APIs. Infrastructure overhead makes self-hosting uneconomical.

Above 5M tokens daily, calculate true cost: GPU rental + monitoring + maintenance vs inference service cost. Self-hosting usually wins at scale.

Q: Which open-source model should I fine-tune?

A: Start with Llama 3.1 70B. It's proven, well-documented, and community tools exist for fine-tuning. Phi-3.5 is good if you want smaller model. DeepSeek V3 is excellent if mathematical reasoning is critical.

Avoid frontier models (Llama 4, Qwen) for fine-tuning unless you have GPU infrastructure. The compute cost of fine-tuning large models is high.

Q: Can open-source models compete with ChatGPT?

A: On most tasks, yes. Llama 4 and DeepSeek V3 match or exceed GPT-4.1. But OpenAI's models are more polished (better instruction-following, fewer quirks). For production systems, OpenAI models have engineering maturity.

For research, cost-sensitive deployments, and specialized tasks, open-source matches or beats proprietary models.

Q: What about proprietary open-source models like LLaMA?

A: Meta's Llama is open-source but commercially backed. This is ideal: community development (open-source model quality) with corporate support (investment, improvements).

Avoid models from unknown sources or heavily restricted licenses. Stick with Llama, DeepSeek, Mistral, Alibaba Qwen, Google Gemma, Microsoft Phi.

Q: Will open-source models catch up to proprietary models?

A: Already have. Llama 4 and DeepSeek V3 match GPT-4.1. OpenAI's advantage is advanced models (o1, GPT-5 Pro) and real-time search. For base model capability, open-source and proprietary are equivalent as of March 2026.

Explore the Best Open-Source LLM Guide for detailed recommendations by use case.

Read Best Ollama Models for practical guidance on running open-source models locally using Ollama.

Learn How to Run AI Locally for step-by-step deployment instructions.

Visit DeployBase LLM Database to compare open-source and proprietary models side-by-side with pricing and benchmarks.

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