馬斯克旗下SpaceXAI發佈Grok 4.5，爲上市後首個重大更新。模型定位通用主力，可完成代碼編寫等任務。馬斯克稱其達到Opus級水平，但速度更快、Token效率更高且成本更低。
英偉達發佈Nemotron-Labs-TwoTower離散擴散語言模型，解決大模型逐token生成速度慢的痛點，權重已在Huggingface開源。該模型複用現有骨幹網預訓練權重，無需從頭訓練，顯著降低成本。採用60B雙塔架構，兩座30B網絡並行協同，每塔激活3B參數並搭載128個可路由專家模塊，提升生成效率。
月之暗面公司繼Kimi 2.7 Code大模型後，推出高速版本，輸出速度提升5-6倍，常規編程下約180 Token/s，大幅縮減等待時間，旨在提升開發者代碼編寫效率。
階躍星辰發佈並開源新一代Flash模型Step 3.7 Flash，專爲智能體生產化落地設計。該模型採用196B參數的稀疏混合專家架構，針對代碼編寫、聯網搜索及多模態工作流進行系統級優化，最高生成速度達每秒400 Tokens，顯著提升高頻場景效率，標誌着國內大模型賽道取得新突破。
Google
$0.49
輸入tokens/百萬
$2.1
輸出tokens/百萬
1k
上下文長度
Openai
$2.8
$11.2
Xai
$1.4
$3.5
2k
$7.7
$30.8
200
Anthropic
$105
$525
$0.7
$7
$35
$17.5
Alibaba
$6
$24
256
-
$4
$16
$2
$20
Bytedance
$0.8
128
$0.15
$1.5
Baidu
32
$10.5
$8
nvidia
Llama-3.3-Nemotron-Super-49B-v1.5是基於Meta Llama-3.3-70B-Instruct的大語言模型，經過多階段後訓練增強了推理和非推理能力。支持128K token上下文長度，在準確性和效率之間取得了良好平衡，適用於推理、聊天和代理任務。
microsoft
微軟研究院開發的首個開源20億參數規模原生1比特大語言模型，在4萬億token語料上訓練完成，證明了原生1比特大語言模型在保持與同規模全精度開源模型相當性能的同時，能顯著提升計算效率。
由微軟研究院開發的開源原生1位大語言模型，參數規模達20億，在4萬億token的語料庫上訓練而成，顯著提升計算效率。
Llama-3.1-Nemotron-Ultra-253B-CPT-v1是基於Meta Llama-3.1-405B-Instruct的大型語言模型，支持128K tokens上下文長度，經過神經架構搜索優化，在準確性和效率之間取得良好平衡。
Notion MCP服務器是一箇中間件服務，通過Notion API實現LLM與Notion工作區的交互，支持Markdown轉換優化token使用效率。
OneMCP是一個通用的MCP協議聚合器，通過元工具架構將多個外部MCP服務器的工具統一管理，採用漸進式發現和語義搜索技術，顯著降低LLM的token消耗並提高工具發現效率。
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
'%3e%3cpath%20d='M55.2622%2038L48.2289%2022L44.1206%2031.3333H47.2539L45.7872%2034.6667H38.7622L46.0956%2018H37.6706C36.7539%2018%2036.0039%2018.75%2036.0039%2019.6667V36.3333C36.0039%2037.25%2036.7539%2038%2037.6706%2038H55.2622Z'%20fill='white'/%3e%3cpath%20d='M64.3368%2018H50.3535L59.1535%2038H64.3368C65.2535%2038%2066.0035%2037.25%2066.0035%2036.3333V19.6667C66.0035%2018.75%2065.2535%2018%2064.3368%2018ZM63.0868%2034.6667H59.7535V21.3333H63.0868V34.6667Z'%20fill='%23FFCC66'/%3e%3cpath%20d='M89.3379%2026.75V29.25H81.8379V36.75H91.8379V24.25H81.8379V26.75H89.3379ZM84.3379%2034.25V31.75H89.3379V34.25H84.3379Z'%20fill='white'/%3e%3cpath%20d='M103.504%2036.75V29.25H96.0039V26.75H103.504V24.25H93.5039V31.75H101.004V34.25H93.5039V36.75H103.504Z'%20fill='white'/%3e%3cpath%20d='M115.17%2026.75V24.25H105.17V36.75H115.17V34.25H107.67V31.75H115.17V29.25H107.67V26.75H115.17Z'%20fill='white'/%3e%3cpath%20d='M80.1712%2029.25V29.175L78.9962%2028L80.1712%2026.825V20.425L78.9962%2019.25H69.3379V36.75H78.9962L80.1712%2035.575V29.25ZM77.6712%2034.25H71.8379V29.25H77.6712V34.25ZM77.6712%2026.75H71.8379V21.75H77.6712V26.75Z'%20fill='white'/%3e%3c/g%3e%3cg%20clip-path='url(%23clip1_644_3948)'%3e%3cpath%20d='M169.59%2022.9827L169.59%2023.6491H177.409L168.175%2032.8822L169.118%2033.8248L178.351%2024.5917L178.352%2032.4106H179.685V22.3164L179.018%2022.3157H169.59V22.9827Z'%20fill='white'/%3e%3c/g%3e%3cdefs%3e%3cclipPath%20id='clip0_644_3948'%3e%3crect%20width='80'%20height='20'%20fill='white'%20transform='translate(36%2018)'/%3e%3c/clipPath%3e%3cclipPath%20id='clip1_644_3948'%3e%3crect%20width='20'%20height='20'%20fill='white'%20transform='translate(164%2018)'/%3e%3c/clipPath%3e%3c/defs%3e%3c/svg%3e)
