Two years ago, open source AI models lagged far behind their closed source counterparts. Today, that gap has narrowed to the point where the decision is no longer about quality alone -- it is about tradeoffs between control, cost, customizability, and operational complexity. This analysis lays out the full picture.

The Current Landscape

Closed Source Leaders - GPT-4o (OpenAI): The commercial benchmark leader with strong all-around performance, multimodal input, and a mature ecosystem. - Claude 3.5 Sonnet (Anthropic): Best-in-class coding performance, 200K context window, and excellent instruction following. - Gemini 2.0 Pro (Google): Competitive reasoning and the deepest integration with Google Cloud infrastructure. - Gemini 2.0 Flash (Google): Exceptional price-performance ratio at $0.10/$0.40 per million tokens.

Open Source Leaders - Llama 3.3 70B (Meta): The most widely deployed open source model. Strong general performance, extensive community support, fine-tuning ecosystem. - Llama 3.1 405B (Meta): The largest open-weights model, competitive with frontier closed source models on many benchmarks. - Qwen 2.5 72B (Alibaba): Particularly strong on multilingual tasks and coding benchmarks. Increasingly popular outside China. - Mistral Large 2 (Mistral AI): European-developed, strong reasoning performance, competitive with GPT-4o on several benchmarks. - DeepSeek-V3 (DeepSeek): Exceptional efficiency and strong benchmark performance, particularly on math and coding tasks.

You can explore the full open source model roster on our open source models page.

Performance Comparison

On MMLU, the gap between the best open source and closed source models is now roughly 3-5 percentage points. Llama 3.1 405B scores around 88.6% compared to GPT-4o's 88.7% -- effectively a tie. At the 70B parameter level, Llama 3.3 70B scores approximately 86%, which is competitive with models that cost 10x more per token.

On coding benchmarks, the picture is more nuanced. Claude 3.5 Sonnet and GPT-4o still lead on SWE-bench Verified (complex, multi-file coding tasks), but on HumanEval and MBPP (single-function generation), DeepSeek-V3 and Qwen 2.5-Coder 32B are within striking distance of the leaders.

On mathematical reasoning (MATH, GSM8K), DeepSeek-V3 is genuinely competitive with closed source frontier models, scoring 90.2% on MATH compared to GPT-4o's 76.6%. This is one area where certain open source models have actually surpassed closed source alternatives.

Check our leaderboard for current benchmark rankings across all models.

Cost Analysis: API vs Self-Hosting

Using Open Source via API Providers

You do not need to self-host to use open source models. Providers like Together AI, Fireworks, Groq, and Anyscale offer hosted inference for open source models:

  • •Llama 3.3 70B via Together: $0.88 input / $0.88 output per million tokens
  • •DeepSeek-V3 via DeepSeek API: $0.27 input / $1.10 output per million tokens
  • •Qwen 2.5 72B via Together: $0.90 input / $0.90 output per million tokens

Compared to GPT-4o at $2.50/$10.00, that is a 3-10x cost advantage with competitive quality for many tasks.

Self-Hosting Economics

Self-hosting becomes cost-effective when your daily volume exceeds roughly 50,000-100,000 requests. The economics depend on your GPU infrastructure:

A single NVIDIA A100 80GB can run Llama 3.3 70B (quantized to 4-bit) at roughly 30-40 tokens/second for a single concurrent request. On AWS, an A100 instance (p4d.24xlarge with 8x A100s) costs approximately $32.77/hour or $23,594/month. That gives you roughly 8 concurrent inference streams processing approximately 200 million tokens per day.

At 200M tokens/day, the effective cost is approximately $0.004 per 1,000 tokens ($3.93 per million), which is cheaper than GPT-4o's input pricing alone. And as volume grows, the per-token cost continues to drop because the GPU cost is fixed.

However, self-hosting has hidden costs: - MLOps engineering time to set up and maintain inference infrastructure - Monitoring, logging, and reliability engineering - Model updates and version management - Scaling and load balancing for variable traffic

For detailed pricing comparisons, visit our pricing page.

Data Privacy and Compliance

This is often the decisive factor for enterprises. When you send data to OpenAI or Anthropic's API, your data is processed on their infrastructure. Both companies offer strong data privacy commitments -- they do not use API data for training by default -- but your data still leaves your perimeter.

With open source models, you can run inference entirely on your own infrastructure or within your cloud VPC. Data never leaves your network. For industries with strict data residency requirements (healthcare, finance, government, EU organizations subject to GDPR), this is not a nice-to-have -- it is a requirement.

Key privacy advantages of open source: - Data never leaves your infrastructure - Full audit trail of model behavior - No dependency on a third party's privacy policy - Compliance with data residency requirements - No risk of a provider policy change affecting your data handling

Customization and Fine-Tuning

Open source models can be fine-tuned on your domain-specific data. This is a substantial advantage for specialized applications:

  • •Customer support: Fine-tune on your product documentation and support transcripts
  • •Legal: Train on your firm's legal documents and case law
  • •Medical: Adapt to clinical notes and medical terminology
  • •Code: Fine-tune on your codebase for internal developer tools

Fine-tuning an open source model on domain-specific data typically produces better results than prompting a frontier closed source model for that specific domain, and at lower per-token inference costs.

Closed source providers are catching up. OpenAI offers fine-tuning for GPT-4o mini and GPT-4o, and Anthropic has expressed plans for fine-tuning APIs. But the flexibility and control of open source fine-tuning remains superior -- you own the resulting weights and can deploy them anywhere.

Latency and Throughput

Closed source APIs have a latency advantage for most users because providers like OpenAI and Anthropic invest heavily in inference optimization, including custom hardware, speculative decoding, and request batching.

Self-hosted open source models can match or beat API latency if you invest in optimization: - Quantization (AWQ, GPTQ, or GGUF formats) reduces memory requirements and increases throughput - vLLM and TensorRT-LLM provide high-performance serving frameworks - Speculative decoding with smaller draft models can significantly reduce time-to-first-token

Groq offers a compelling middle ground: their custom LPU hardware delivers extremely low latency for open source models (often sub-200ms time-to-first-token for Llama 70B), making it possible to get self-hosting-like performance without managing infrastructure.

Ecosystem and Support

Closed source models have more polished ecosystems. OpenAI's platform includes assistants, function calling, file storage, vector search, and extensive documentation. Anthropic's tools are more focused but equally well-documented.

Open source models have the broader community. Hugging Face alone hosts thousands of fine-tuned variants of Llama and Mistral for specific use cases. The LangChain, LlamaIndex, and vLLM communities provide extensive tooling. If you hit an edge case, the open source community is often more responsive than a support ticket.

When to Choose Closed Source

  • •You need the absolute highest performance on complex reasoning tasks
  • •Your team lacks MLOps expertise for self-hosting
  • •Low volume makes API pricing more economical than GPU infrastructure
  • •You want a batteries-included platform with built-in tools and integrations
  • •Rapid prototyping and time-to-market are your priority

When to Choose Open Source

  • •Data privacy and data residency are hard requirements
  • •Your daily request volume exceeds 50,000+ requests
  • •You need fine-tuning on domain-specific data
  • •You want to avoid vendor lock-in to a single provider
  • •Your use case is well-served by a 70B-parameter model (most tasks are)
  • •You need full control over model behavior and deployment

The Hybrid Approach

Many production systems use both. A common pattern:

  • •Route complex, high-stakes tasks (contract analysis, customer-facing content, difficult code) to GPT-4o or Claude 3.5 Sonnet
  • •Route high-volume, simpler tasks (classification, extraction, summarization) to self-hosted Llama 3.3 70B or a hosted open source API
  • •Use open source models for development and testing, closed source for production quality checks

This hybrid approach captures the cost benefits of open source while maintaining the quality ceiling of frontier closed source models.

Looking Ahead

The trend is clear: open source models are closing the gap faster than closed source models are pulling ahead. Meta's commitment to open weights with Llama, DeepSeek's remarkable efficiency breakthroughs, and Alibaba's investment in Qwen suggest that within 12-18 months, the performance difference between the best open source and closed source models may be negligible for most practical applications.

The winning strategy is not to bet exclusively on either approach, but to build your infrastructure to leverage both. Explore the full model landscape on GPTCrunch to find the right mix for your needs.