Open source LLMs in 2026 have closed the quality gap with proprietary models to the point where, for many tasks, they are competitive. Llama 3.3 70B scores approximately 86-87% on MMLU (Massive Multitask Language Understanding), compared to GPT-4o at approximately 88.7%. On HumanEval (code generation), Llama 3.3 70B scores around 80-82% versus GPT-4o at approximately 90%. The gap is real but narrowing. Where open source still consistently loses: multimodal tasks (no open source vision-language model matches GPT-4o Vision), complex multi-step tool use reliability, and consistency on ambiguous or complex instructions. Where open source wins: cost, privacy, customizability, and for specific tasks where fine-tuning makes a 7-8B model outperform generic 70B+ models.
Understanding the Benchmarks
Before comparing numbers, it helps to understand what benchmarks actually measure.
MMLU (Massive Multitask Language Understanding): 57 subject areas from STEM to humanities, multiple-choice questions. Tests breadth of knowledge and reasoning. High MMLU score means the model has absorbed a lot of world knowledge and can apply it to standardized questions. Does not test instruction following, code generation, or real-world task completion well.
HumanEval: 164 Python programming problems with unit tests. Tests the ability to write correct code for algorithmic problems. Strong correlation with practical code generation ability, though real codebases are more complex than HumanEval problems.
MATH: 12,500 competition math problems. Tests mathematical reasoning. High variance across models. GPT-4o and o1 significantly outperform open source models here.
MT-Bench: LLM-judged multi-turn conversation quality benchmark. More correlated with real-world instruction following than MMLU.
LMSYS Chatbot Arena: Human preference voting from blind pairwise comparisons. The most realistic benchmark for conversational quality. GPT-4o and Claude Sonnet consistently lead this ranking, though open source models have strong showings in specific categories.
Current Benchmark Numbers (Early 2026)
These are approximate figures based on published evaluations. Model quality changes rapidly as new versions are released.
Closed source:
- GPT-4o: MMLU ~88.7%, HumanEval ~90%, MT-Bench ~9.0/10
- Claude 3.5 Sonnet: MMLU ~88.3%, HumanEval ~92%, MT-Bench ~9.0/10
- Gemini 1.5 Pro: MMLU ~85.9%, HumanEval ~84.1%
Open source (large):
- Llama 3.3 70B: MMLU ~86.0%, HumanEval ~80.5%, MT-Bench ~8.7/10
- Mistral Large 2: MMLU ~84.0%, HumanEval ~82.0%
- Qwen 2.5 72B: MMLU ~86.1%, HumanEval ~86.6%
Open source (small):
- Llama 3.2 3B: MMLU ~63.4%, HumanEval ~57.0%
- Llama 3.1 8B: MMLU ~73.0%, HumanEval ~72.6%
- Mistral 7B v0.3: MMLU ~64.1%, HumanEval ~60.0%
- Qwen 2.5 7B: MMLU ~74.2%, HumanEval ~72.0%
Qwen 2.5 72B's HumanEval score of ~86.6% actually exceeds GPT-4o's ~90% claimed score on some independent evaluations. The benchmark picture is more competitive than it was 12 months ago.
