Open Source LLM Leaderboard 2026: The 12 Best Models Right Now

The open source LLM landscape in March 2026 barely resembles what it looked like a year ago. Chinese labs now hold most top positions. Models from Moonshot, Zhipu, and Alibaba consistently match or beat GPT-4o on major benchmarks. And the "small" models are getting scary good — Qwen 3.5 27B threatens models ten times its size.

This is our definitive ranking based on benchmark data from MMLU-Pro, GPQA Diamond, HumanEval, SWE-bench Verified, AIME 2025, and Chatbot Arena ELO. Not vibes — numbers.

The Tier List

S-Tier: Best in Class

1. Kimi K2.5 (Moonshot) — 1T parameters

The benchmark king. Kimi K2.5 leads or ties in nearly every category: 99.0% HumanEval (coding), 96.1% AIME 2025 (math), 87.6% GPQA Diamond (graduate-level science), and 76.8% SWE-bench Verified (real software engineering). At 1T parameters with 262K context, it's enormous — but the results speak for themselves.

Best for: Teams that need the absolute best open-source performance and have the infrastructure to run a 1T model.

Catch: You need serious hardware — multiple A100/H100 GPUs. Not a local model.

2. Qwen 3.5 (Alibaba) — 397B parameters

The most well-rounded model on the leaderboard. Qwen 3.5 has the highest GPQA Diamond score of any open-source model (88.4%), the best IFEval (92.6%, meaning it follows complex instructions almost perfectly), and strong performance everywhere else. Chatbot Arena ELO of 1450 puts it in the top tier for real-world conversations.

Best for: The model most likely to replace a closed-source API for general use. If you could only pick one, this is it.

3. GLM-5 (Zhipu AI) — 744B parameters

Highest SWE-bench score of any open model (77.8%). GLM-5 is the best open-source model for real-world software engineering tasks. It also scores well on Chatbot Arena (1454 ELO — highest on our list). The tradeoff: weaker on MMLU-Pro (70.4%) and LiveCodeBench (52.0%), suggesting it excels at practical engineering over academic benchmarks.

Best for: Software engineering teams, coding agents, complex multi-step development tasks.

A-Tier: Excellent All-Rounders

4. DeepSeek V3.2 — 685B parameters

The latest DeepSeek iteration pushes MMLU-Pro to 85.0% and GPQA Diamond to 79.9%. The standout is LiveCodeBench (74.1%) — a benchmark that tests coding on problems released after training cutoff, making it harder to game. SWE-bench at 67.8% is solid. A reliable workhorse for any serious deployment.

Best for: Production API deployments where you want consistent quality across tasks.

5. DeepSeek R1 — 671B parameters

The reasoning specialist. DeepSeek R1's explicit chain-of-thought reasoning produces AIME 2025 scores of 87.5% and MMLU-Pro of 84.0%. HumanEval at 90.2% confirms strong coding ability. The reasoning traces are visible, which makes it great for educational contexts and verifiable outputs.

Best for: Math, science, logic, and tasks where you need to see and verify the model's reasoning process.

Runs locally: The 14B distilled version is excellent in Ollama — one of our top 10 recommended models.

6. GLM-4.7 (Zhipu AI) — 355B parameters

The smaller Zhipu model that punches above its weight. GPQA Diamond at 85.7%, LiveCodeBench at 84.9% (highest on the entire leaderboard), and HumanEval at 94.2%. At "only" 355B parameters, it's more practical to deploy than GLM-5 while matching or beating it on several benchmarks.

Best for: Coding tasks where you want GLM-quality without GLM-5's infrastructure requirements.

B-Tier: Strong Specialists

7. MiMo-V2-Flash (Xiaomi) — 309B parameters

The efficiency surprise. Xiaomi's MiMo-V2-Flash delivers 80.6% on LiveCodeBench and 73.4% on SWE-bench — competitive with DeepSeek V3.2 and close to Kimi K2.5 — at roughly half the parameter count. GPQA Diamond at 83.7% is excellent. This is the model to watch if you want near-S-tier performance with less compute.

Best for: Cost-conscious production deployments that still need top-tier coding ability.

8. GPT-oss 120B (OpenAI) — 117B parameters

OpenAI's first serious open-source release. MMLU-Pro leads the entire leaderboard at 90.0%, and AIME 2025 at 97.9% is the highest math score of any open model. At 117B parameters, it's far more deployable than the 400B+ giants. The weakness: SWE-bench at 62.4% and no LiveCodeBench data suggest it's better at knowledge tasks than practical software engineering.

Best for: Knowledge-intensive tasks, educational applications, and math — especially if you want a smaller deployable model.

9. Step-3.5-Flash (Stepfun) — 196B parameters

Under the radar but impressive. LiveCodeBench at 86.4% is one of the highest scores on the leaderboard. AIME 2025 at 99.8% — virtually perfect on competition math. At 196B, it's mid-sized. The name "Flash" suggests inference speed optimizations, making it practical for latency-sensitive applications.

Best for: Math competitions, coding benchmarks, latency-sensitive deployments.

10. Nemotron Super 49B (NVIDIA) — 49B parameters

NVIDIA's entry in the "actually runnable on consumer hardware" category. IFEval at 88.6% and AIME 2025 at 82.7% on a 49B model is remarkable. MATH at 97.4% shows strong quantitative reasoning. This runs on a single RTX 4090 at Q4 quantization — making it the best performing model you can run locally without multi-GPU setups.

Best for: The best "big model" you can run on a single 24GB GPU. See our GPU guide for hardware recommendations.

C-Tier: Notable Models

11. Llama 4 Maverick (Meta) — 400B parameters

Meta's latest disappointed relative to expectations. Chatbot Arena at 1328 ELO is the lowest on this list, and missing benchmarks (no SWE-bench, no LiveCodeBench, no AIME) make it hard to evaluate fairly. The 1M context window is impressive but unproven at scale. The real value may be in fine-tuning — Meta's models have the largest ecosystem.

Best for: Teams that need a 1M context window or plan to fine-tune extensively.

12. Gemma 3 27B (Google) — 27B parameters

The best model under 30B parameters. Gemma 3 27B won't compete with the 300B+ giants on benchmarks, but at 27B it runs on a single 16GB GPU. 128K context, native multimodal support, and strong instruction-following make it the practical choice for resource-constrained local deployments.

Best for: Local use on 16-24GB GPUs. One of our top Ollama picks.

Ranking by Task

Best for Coding

1. Kimi K2.5 (HumanEval 99.0%, SWE-bench 76.8%)
2. GLM-4.7 (HumanEval 94.2%, LiveCodeBench 84.9%)
3. GLM-5 (SWE-bench 77.8%)

Best for Math & Reasoning

1. Step-3.5-Flash (AIME 99.8%)
2. GPT-oss 120B (AIME 97.9%)
3. Kimi K2.5 (AIME 96.1%)

Best for General Knowledge

1. GPT-oss 120B (MMLU-Pro 90.0%)
2. Qwen 3.5 (MMLU-Pro 87.8%)
3. Kimi K2.5 (MMLU-Pro 87.1%)

Best for Instruction Following

1. Qwen 3.5 (IFEval 92.6%)
2. Kimi K2.5 (IFEval 94.0%)
3. GLM-5 / GLM-4.7 (IFEval 88.0%)

Best for Running Locally

1. Gemma 3 27B (16GB VRAM)
2. Nemotron Super 49B (24GB VRAM at Q4)
3. GPT-oss 120B (2x 24GB GPUs at Q4)

How to Run These Models

The top models (400B+) require cloud GPU clusters — think H100s or A100s. But several excellent options run locally:

On a single GPU:

• Gemma 3 27B → 16GB VRAM → ollama pull gemma3:27b
• Nemotron Super 49B → 24GB VRAM → quantized GGUF via Ollama
• DeepSeek R1 14B (distilled) → 10GB VRAM → ollama pull deepseek-r1:14b

For production serving at scale:

Use vLLM for maximum throughput — it delivers 19x the throughput of Ollama for concurrent requests.

GPU Recommendations

To run the best models locally:

• Budget (16GB): RTX 4060 Ti 16GB — runs Gemma 3 27B and all sub-14B models
• Sweet spot (24GB): NVIDIA RTX 4090 — runs everything up to 49B quantized, fast inference
• Future-proof (32GB): NVIDIA RTX 5090 — 32GB unlocks 70B+ models at full quality

Disclosure: GPU links above are Amazon affiliate links. We earn a commission at no extra cost to you. These are the same GPUs we recommend regardless of affiliate status.

The Trend: China Dominates, Small Models Close the Gap

Three observations from the 2026 leaderboard:

1. Chinese labs hold 7 of the top 12 positions. DeepSeek, Qwen, Zhipu, Moonshot, Xiaomi, and Stepfun are all shipping state-of-the-art open models. The era of American dominance in open-source AI is over.

2. The gap between open and closed is nearly gone. Kimi K2.5 and Qwen 3.5 match or exceed GPT-4o on most benchmarks. The remaining advantage of closed models (Claude 4, Gemini Ultra) is increasingly about UX and ecosystem, not raw capability.

3. Smaller models are catching up fast. GPT-oss at 117B competes with 600B+ models on knowledge tasks. Nemotron Super at 49B is remarkably capable. The next frontier isn't bigger models — it's getting S-tier quality into models that run on a single GPU.

This leaderboard will look different in 3 months. We'll keep it updated.

Related: Best Ollama Models 2026 | vLLM vs Ollama vs TGI | Best GPU for AI 2026

Related Articles

• Llama 3 vs Mistral vs Phi-4: Which Open Source LLM Wins in 2026?
• DeepSeek vs Llama vs Qwen: Best Open-Source LLM for Local Use (2026)

FAQ

What is the best open-source LLM in 2026?

DeepSeek R1 leads on reasoning benchmarks. Llama 3.3 70B is the best general-purpose model. Qwen 3 32B offers the best quality-to-size ratio. For coding, DeepSeek Coder V2 and Qwen 2.5 Coder 32B lead.

What is HumanEval and why does it matter?

HumanEval is a benchmark of 164 Python programming problems measuring code generation quality. GPT-4 scores ~90%; top open-source models (DeepSeek Coder V2, Qwen 2.5 Coder) reach 85-88%. It's the standard metric for comparing coding LLMs.

What is MMLU?

MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects. Scores are 0-100%, random chance is 25%. Current frontier models score 85-90%+. It measures breadth of factual knowledge more than reasoning.

How do I choose between 7B and 70B models?

Choose 7B: limited VRAM (under 16GB), need fast responses, simple tasks. Choose 70B: complex reasoning, creative writing, nuanced instruction following. The quality jump from 7B to 70B is significant for complex tasks.

Where can I find up-to-date LLM rankings?

LMSYS Chatbot Arena (lmsys.org) tracks human preference rankings daily. HuggingFace Open LLM Leaderboard tracks benchmark scores. EQ-Bench and LiveCodeBench are specialized newer benchmarks. Check multiple for a complete picture.