GPT-Realtime-2
OpenAI's second-generation real-time audio model with GPT-5-class reasoning, 128K context, five reasoning levels, and parallel tool calling - now generally available in the Realtime API.
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
GPT-5.5 Instant
OpenAI's new default ChatGPT model cuts hallucinations by 52.5% and adds Gmail-backed personalization while maintaining the low latency of its predecessor.
Best Coding Models on OpenRouter - Opus 4.7 Rivals
Claude Opus 4.7 scores 87.6% on SWE-bench Verified but costs $5/$25 per million tokens. These four models match or near-match its coding performance at a fraction of the price on OpenRouter.
Best AI Models for Language Translation - May 2026
Gemini 3.1 Pro leads verified 2026 benchmarks at $2 per million tokens while GPT-5.5 and Claude Opus 4.7 postdate available translation evaluations - rankings, scores, and pricing for 10 models.
AI Agent Memory in 2026: 5 Frameworks Ranked
We compared Mem0, Zep, Letta, LangMem, and Cognee on architecture, benchmarks, pricing, and use cases to find the right memory layer for your agent stack.
Fine-Tuning Costs Comparison - Train Your Own AI
May 2026: Together AI adds Llama 4 and DeepSeek fine-tuning, Fireworks raised deployment prices $1/hr, and H100 rentals fell to under $2.40/hr.
GPT-5.5 vs Claude Opus 4.7: Benchmarks and Pricing
GPT-5.5 and Claude Opus 4.7 both launched in April 2026 with 1M context windows and agentic coding focus. One leads on math and long-context retrieval, the other on software engineering and vision.
Cerebras WSE-3 - The Wafer-Scale AI Engine
The Cerebras WSE-3 is the largest chip ever built - a TSMC 5nm wafer with 900,000 AI cores, 44GB SRAM, and 21 PB/s bandwidth. Now powering a $20B OpenAI deal and Amazon Bedrock deployments.
Google TPU 8i - Low-Latency Inference for Agent Era
Google's TPU 8i is a purpose-built inference chip with 10.1 FP4 PFLOPs, 288GB HBM3e at 8,601 GB/s, and a Boardfly topology that cuts collective latency 5x for agentic AI workloads.
Google TPU 8t - AI Training at ExaFLOP Scale
Google's TPU 8t packs 12.6 FP4 PFLOPs and 216GB HBM3e per chip, scaling to 9,600-chip superpods with 121 ExaFLOPS and 2 petabytes of shared HBM for massive model training.
Qualcomm AI250 - Near-Memory Computing for Inference
The Qualcomm AI250 applies near-memory computing to the same 768GB LPDDR5X design as the AI200, promising 10x higher effective memory bandwidth and lower power for LLM inference at rack scale.
Rebellions RebelRack - 64 FP8 PFLOPs at 5 Kilowatts
The Rebellions RebelRack packs 32 Rebel100 chiplet NPUs with 4.5TB HBM3E and 153.6 TB/s aggregate bandwidth into a rack drawing just 5kW - roughly 4x the compute-per-watt of an H100 DGX.
