New SOTA. Tied with Gemini 3.1 Pro (94.3%). PhD-level science reasoning.

Highest GPQA Diamond score ever recorded. Leads 13 of 16 major benchmarks per Google DeepMind.

OpenAI Pro-tier model. Surpasses PhD experts (69.7%) by large margin. Just below Gemini 3.1 Pro (94.3%).

GPT-5.4 scores 92.8% on GPQA Diamond, marginal improvement over GPT-5.2 at 92.4%

GPT-5.2 Thinking variant. +4.3% over GPT-5.1. No tools, max reasoning effort.

Announced with Gemini 3 launch. PhD-level reasoning, top of LMArena at 1501 Elo.

Third-highest GPQA Diamond score. Codex-native agent pairing frontier coding with general reasoning.

Graduate-level science reasoning. Within 1.1 points of GPT-5.2.

Qwen 3.6 Plus leads on GPQA with 90.4%

Frontier PhD-level reasoning at Flash-tier pricing and latency. Matches larger models.

Averaged over 10 trials with adaptive thinking. Within 2.5 pts of GPT-5.2.

Strong PhD-level reasoning. Ahead of Gemini 3.1 Pro (90.8%) and GLM-5.1 (86.2%).

ByteDance frontier model. Gold medals on ICPC, IMO, CMO. Competes with GPT-5.2.

397B total, 17B active MoE. Native multimodal with vision. Top open-weight model on reasoning.

All-time high GPQA Diamond at time of release. Verified by Artificial Analysis.

Small/cheap GPT-5.4 variant approaching full GPT-5.4 (92.8%) on reasoning. 2x faster than GPT-5 mini.

OpenAI reasoning model. Strong scientific knowledge but below Gemini 3.1 Pro (94.3%) and Claude Opus 4.6 (91.3%).

1T MoE, 32B active. Open-source. Averaged over 8 runs. Strong but trails Gemini 3.1 Pro (94.3) and Claude Opus 4.6 (91.3).

Lightweight model rivaling larger Gemini 3 series on reasoning. 2.5x faster than 2.5 Flash.

Self-improving model that ran 100+ autonomous optimization cycles during training. Competitive with Kimi K2.5 on science reasoning.

Strong reasoning for a 744B MoE open-weight model. Behind Gemini 3.1 Pro (94.3) and GPT-5.4 (92.8).

Open-weight 744B MoE (40B active). Competitive with proprietary models, trails only Gemini 3 Pro and GPT-5.2.

Highest recorded result among open-weights models under 40B parameters on GPQA Diamond

2nd-highest among open-weights models under 40B parameters on GPQA Diamond

Deep reasoning variant. Par with Gemini-3.0-Pro on scientific reasoning.

Open-weight, 400B params. Strong science reasoning for an open model.

Dense 27B model, natively multimodal. Competitive with much larger frontier models on graduate-level reasoning.

Strong graduate-level reasoning. Between Gemini 3 Pro (91.0) and Claude Sonnet 4.5 (83.0).

Strong for a mid-tier model. 262K context window, multimodal.

From Google model card. Dense 31B instruction-tuned.

Open-weight MoE (196B total, 11B active). From official model card.

Smallest GPT-5.4 variant. Strong GPQA for its cost tier ($0.20/1M input tokens).

Graduate-level science reasoning. Solid mid-frontier performance.

MoE with 3.8B active params. Outperforms Sonnet 4.6 (74%). Runs on 24GB hardware.

9B params. Outperforms GPT-OSS-120B (80.1). Best reasoning-per-parameter ratio among open models.

Strong scientific reasoning from OpenAI cost-efficient reasoning model. Competitive with much larger models.

India's first domestically-trained 105B model. MoE with 10.3B active params. Apache 2.0.

Trinity-Large-Thinking: 398B MoE, only 13B active params. Open-weight, Apache 2.0.

Remarkable for a 4B model. Matches larger Qwen3-80B-A3B on reasoning.

30B-A3B MoE, 3.6B active params. Competitive with much larger models on graduate-level reasoning.

Primary source (HuggingFace model card, arxiv paper). Scores up to 80.9% reported with tool augmentation.

71.2% on GPQA Diamond with efficient output length. Released March 16, 2026.

17B active params, 128 experts, 400B total. Released alongside Scout on April 5. MoE architecture, natively multimodal.

14B SFT+RL model. Outperforms DeepSeek-R1-Distill-70B on most benchmarks.

Without tool augmentation. HuggingFace model card primary source. 20.9B total params, 3.6B active.

14B model beating o1-mini (60.0%) and QwQ-32B (59.5%) on graduate-level science.

Strong graduate-level reasoning for a 4B edge model.

17B active params, 16 experts. 10M context window. Smaller expert count than Maverick.

Ultra-compact model with Per-Layer Embeddings. Competitive with models 10x its size on science reasoning.