A curated collection of technical reports related to TOP large language models. Hope it helps!
Qwen: https://arxiv.org/abs/2309.16609
Qwen-VL: https://arxiv.org/abs/2308.12966
Qwen2: https://arxiv.org/abs/2407.10671
Qwen2-VL: https://arxiv.org/abs/2409.12191
Qwen2-Audio: https://export.arxiv.org/abs/2407.10759
Qwen2.5: https://arxiv.org/abs/2412.15115
Qwen2.5-Coder: https://arxiv.org/abs/2409.12186
Qwen2.5-Math: https://arxiv.org/abs/2409.12122
Qwen2.5-1M: https://arxiv.org/abs/2501.15383
Qwen2.5-VL: https://arxiv.org/abs/2502.13923
Qwen3: https://arxiv.org/abs/2505.09388
Qwen3-VL: https://arxiv.org/abs/2511.21631
Qwen3-Omni: https://arxiv.org/abs/2509.17765
Qwen3-Embedding: https://arxiv.org/abs/2506.05176v2
Qwen3-VL-Embedding: https://arxiv.org/abs/2601.04720
QwQ: https://qwenlm.github.io/blog/qwq-32b-preview/
QvQ: https://qwenlm.github.io/blog/qvq-72b-preview/
QwenLong-L1: https://www.arxiv.org/abs/2505.17667
QwenLong-L1.5: https://arxiv.org/abs/2512.12967v1
WorldPM: https://arxiv.org/abs/2505.10527
QwenStyle: https://arxiv.org/abs/2601.06202
Deepseek: https://arxiv.org/abs/2401.02954
Deepseek-v2: https://arxiv.org/abs/2405.04434
Deepseek-v3: https://arxiv.org/abs/2412.19437
Deepseek-v3 insight: https://arxiv.org/pdf/2505.09343
Deepseek-v3.1: https://api-docs.deepseek.com/news/news250821
Deepseek-v3.2: https://arxiv.org/abs/2512.02556
Deepseek-R1: https://arxiv.org/abs/2501.12948
Janus: https://arxiv.org/abs/2410.13848
Janus-pro: https://github.com/deepseek-ai/Janus/blob/main/janus_pro_tech_report.pdf
GPT: https://arxiv.org/abs/2305.10435
GPT-3: https://arxiv.org/abs/2005.14165
GPT-4: https://arxiv.org/abs/2303.08774
GPT-4o: https://openai.com/index/hello-gpt-4o/
GPT-4o-mini: https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/
GPT-o1: https://openai.com/index/learning-to-reason-with-llms/
GPT-o1-mini: https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning/
GPT-o3-mini: https://openai.com/index/openai-o3-mini/
GPT-5: https://cdn.openai.com/gpt-5-system-card.pdf
GPT-5.1: https://openai.com/zh-Hans-CN/index/gpt-5-1/
GPT-5.2: https://openai.com/zh-Hans-CN/index/introducing-gpt-5-2/
LLaMA: https://arxiv.org/abs/2302.13971
LLaMA-2: https://arxiv.org/abs/2307.09288
LLaMA-3: https://arxiv.org/abs/2407.21783
LLaMA-3.1: https://ai.meta.com/blog/meta-llama-3-1/
LLaMA-3.2: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/
LLaMA-3.3: https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3/
LLaMA-4: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
Claude: https://www.anthropic.com/news/introducing-claude
Claude 2: https://www.anthropic.com/news/claude-2
Claude pro: https://www.anthropic.com/news/claude-pro
Claude 2.1: https://www.anthropic.com/news/claude-2-1
Claude 3: https://www.anthropic.com/news/claude-3-family
Claude 3: https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf
Claude 3.5: https://www.anthropic.com/news/3-5-models-and-computer-use
Claude 3.7: https://www.anthropic.com/news/claude-3-7-sonnet
Claude 4: https://www.anthropic.com/news/claude-4
Claude 4.5 sonnet: https://www.anthropic.com/news/claude-sonnet-4-5
Claude 4.5 opus: https://www.anthropic.com/news/claude-opus-4-5
Gemini: https://arxiv.org/abs/2312.11805
Gemini 1.5: https://arxiv.org/abs/2403.05530
Gemini 2.0: https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/#ceo-message
Gemini 2.5: https://arxiv.org/abs/2507.06261
Gemini 3.0: https://deepmind.google/models/gemini/
Gemini 3.0 Pro: https://deepmind.google/models/gemini/pro/
Gemma 1.0: https://arxiv.org/abs/2403.08295
Gemma 2.0: https://arxiv.org/abs/2408.00118
Gemma 3.0: https://arxiv.org/abs/2503.19786
Gemma 3n: https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
CodeGemma: https://arxiv.org/abs/2406.11409
ShieldGemma: https://arxiv.org/abs/2407.21772
ShieldGemma2: https://arxiv.org/abs/2504.01081v2
TxGemma: https://arxiv.org/abs/2504.06196
MedGemma: https://arxiv.org/abs/2507.05201
EmbeddingGemma: https://arxiv.org/abs/2509.20354
VaultGemma: https://arxiv.org/abs/2510.15001
Phi-1.5: https://arxiv.org/abs/2309.05463
Phi-2: https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
Phi-3: https://arxiv.org/abs/2404.14219
Phi-4: https://arxiv.org/abs/2412.08905
Phi-4-mini: https://arxiv.org/abs/2503.01743
Mistral 7B: https://arxiv.org/abs/2310.06825
Linq-Embed-Mistral: https://arxiv.org/abs/2412.03223
Magistral: https://arxiv.org/abs/2506.10910
Ministral 3: https://arxiv.org/abs/2601.08584
Official Research Web: https://mistral.ai/news?category=research
intellect-1: https://arxiv.org/abs/2412.01152
intellect-2: https://arxiv.org/abs/2505.07291
intellect-3: https://arxiv.org/abs/2512.16144
Skywork R1V: https://arxiv.org/abs/2504.05599
Skywork R1V2: https://arxiv.org/abs/2504.16656
Skywork R1V3: https://arxiv.org/abs/2507.06167
Skywork R1V4: https://arxiv.org/abs/2512.02395
MiniCPM: https://arxiv.org/abs/2404.06395
MiniCPM-v: https://arxiv.org/abs/2408.01800
MiniCPM3: https://huggingface.co/openbmb/MiniCPM3-4B
MiniCPM4: https://arxiv.org/abs/2506.07900
Baichuan-Omin: https://arxiv.org/abs/2410.08565
Baichuan-Omni-1.5: https://arxiv.org/abs/2501.15368
Baichuan Alignment: https://arxiv.org/abs/2410.14940
Baichuan2: https://arxiv.org/abs/2309.10305
Baichuan2-Sum: https://arxiv.org/abs/2401.15496
Yi: https://arxiv.org/abs/2403.04652
Yi-Coder: https://01-ai.github.io/blog.html?post=en/2024-09-05-A-Small-but-Mighty-LLM-for-Code.md
Yi-Lightning: https://arxiv.org/abs/2412.01253