Destination
Higher token consumption can reduce the efficiency of open reasoning models

Open-weight reasoning models often use far more tokens than closed models, making them less efficient per query, according to Nous Research.<br /> The article Higher token consumption can reduce the efficiency of open reasoning models appeared first on THE DECODER. [...]

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venturebeat
Microsoft built Phi-4-reasoning-vision-15B to know when to think — and when thinking is a waste of time

Microsoft on Tuesday released Phi-4-reasoning-vision-15B, a compact open-weight multimodal AI model that the company says matches or exceeds the performance of systems many times its size — while co [...]

Match Score: 188.02

venturebeat
1Password moves into AI cost management, betting that token spend is the next enterprise budget crisis

1Password on Tuesday launched AI Spend and Consumption Management, a new capability embedded in its SaaS Manager platform that gives IT and finance teams a unified, real-time view of how their organiz [...]

Match Score: 181.91

venturebeat
How DeepSeek’s radical architecture is shattering Silicon Valley's token moat

DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]

Match Score: 114.63

venturebeat
New training method boosts AI multimodal reasoning with smaller, smarter datasets

Researchers at MiroMind AI and several Chinese universities have released OpenMMReasoner, a new training framework that improves the capabilities of language models in multimodal reasoning.The framewo [...]

Match Score: 106.55

venturebeat
'Western Qwen': IBM wows with Granite 4 LLM launch and hybrid Mamba/Transformer architecture

IBM today announced the release of Granite 4.0, the newest generation of its homemade family of open source large language models (LLMs) designed to balance high performance with lower memory and cost [...]

Match Score: 103.96

venturebeat
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]

Match Score: 103.15

venturebeat
Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TT [...]

Match Score: 99.37

venturebeat
Moonshot's Kimi K2 Thinking emerges as leading open source AI, outperforming GPT-5, Claude Sonnet 4.5 on key benchmarks

Even as concern and skepticism grows over U.S. AI startup OpenAI's buildout strategy and high spending commitments, Chinese open source AI providers are escalating their competition and one has e [...]

Match Score: 98.37

venturebeat
How to build custom reasoning agents with a fraction of the compute

Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensive models or relying [...]

Match Score: 94.26