DeepSeek V4 vs Claude Opus 4.6 - Open Weight Meets Proprietary
A pre-release comparison of DeepSeek V4 and Claude Opus 4.6 - the open-weight challenger that could match Opus on coding at potentially 89x lower output cost.
James Kowalski
AI Benchmarks & Tools Analyst
James is a software engineer turned tech writer who spent six years building backend systems at a fintech startup in Chicago before pivoting to full-time analysis of AI tools and infrastructure. His engineering background means he doesn't just read the spec sheet - he runs the benchmarks, profiles the latency, and checks whether the marketing claims hold up under real workloads.
He studied Computer Science at the University of Illinois at Urbana-Champaign, where he first got hooked on natural language processing during a senior research project on sentiment analysis. He later completed a certificate in data journalism from Northwestern's Medill School.
At Awesome Agents, James owns the leaderboards and tool comparison coverage. He maintains the site's benchmark tracking methodology and is the person who actually runs the numbers before publishing any ranking. He is also an open-source advocate and contributes to several projects in the LLM inference space.
Based in Chicago, IL.
Articles by James Kowalski
DeepSeek V4 vs Kimi K2.5 - China's Trillion-Parameter MoE Duel
Two Chinese open-weight trillion-parameter MoE models with ~32B active parameters each - DeepSeek V4 bets on cost and context, Kimi K2.5 bets on Agent Swarm and verified benchmarks.
DeepSeek V3.2 vs V4 - What Changes With a Trillion Parameters
A pre-release comparison of DeepSeek V3.2 and V4 - examining the generational leap from 671B text-only to a trillion-parameter natively multimodal model with 1M context.
GPT-5.2
GPT-5.2 is OpenAI's most capable model with three modes, 400K context, and record-setting professional benchmarks - but speed and pricing raise questions.
AMD Instinct MI300X
AMD Instinct MI300X specs, benchmarks, and real-world performance data. 192GB HBM3, 5,300 GB/s bandwidth, 2,610 TFLOPS FP8 on CDNA 3 chiplet architecture.
AMD Instinct MI350X
AMD Instinct MI350X specs and performance estimates. 288GB HBM3e, ~6,000 GB/s bandwidth, ~3,600 TFLOPS FP8 on CDNA 4 architecture at TSMC 3nm.
Apple M4 Max - Unified Memory for Large Models
Full specs and benchmarks for the Apple M4 Max SoC - up to 128GB unified memory at 546 GB/s, 3nm process, and why it has become the quiet favorite for running 70B+ models locally.
AWS Trainium2 - Amazon's Cloud Training Chip
AWS Trainium2 is Amazon's second-generation custom AI training chip, powering EC2 Trn2 instances with 96GB HBM2e per chip and tight integration with the AWS Neuron SDK and SageMaker ecosystem.
Cambricon MLU590 - China's Inference Accelerator
Full specs and analysis of the Cambricon MLU590 - 192GB HBM2e, ~2,400 GB/s bandwidth, TSMC 7nm, and what it means for AI inference outside the NVIDIA ecosystem.
Google TPU v6e Trillium
Google Cloud TPU v6e Trillium specs, benchmarks, and pricing. 32GB HBM per chip, ~1,600 GB/s bandwidth, optimized for Transformer training and inference at cloud scale.
Google TPU v7 Ironwood
Google TPU v7 Ironwood specs, architecture, and performance estimates. Google's next-gen inference-optimized TPU with massive memory per chip, announced at Cloud Next 2025.
Groq LPU - Deterministic Inference at Scale
Groq's Language Processing Unit (LPU) is a purpose-built inference ASIC that trades HBM for 230MB of on-chip SRAM, delivering deterministic latency and record-breaking tokens-per-second for LLM serving.
