Reviews and head-to-head comparisons of new AI models, closed and open, with benchmark results and plain explainers of what each model does well.
Ornith-1.5-122B posts 76.9 on SWE-bench Pro and 84.5 on Verified. Here is how it compares with Qwen3.8, Nemotron 3.5 Super VL, DeepSeek-V4-Flash and Beam.
Tencent's Hy-Memory-30B-A3B memory writer averages 55.49 across four harnesses vs Claude Opus 5 at 54.62. Per-harness and per-benchmark scores inside.
Spark-X2.6-4B claims best-in-class small-model results and efficiency on par with Qwen3.5-4B and Gemma-E4B. What the card says and what it leaves out.
DeepSeek-V4.1-Flash-1017 gains 11.1 points on Terminal-Bench 4.0 over V4.1-Flash. See how it stacks up against Opus 5, GPT-5.6 Sol, GLM-5.3 and K3.
NVIDIA's open-weight Nemotron 3.5 Super VL uses Mamba-2, MoE and attention with 120B total, 12B active parameters and 1M context. Here is where it fits.
Hands-on results for NVIDIA's open Nemotron 3.5 Super VL (120B, 12B active): bug fixing, vision, translation, circuits, and benchmarks.
Ornith-1.5-122B tested on a Docker bug fix, vision, screenshot reasoning, creative HTML and 80-language translation. Here is where it held up and failed.
GPT-6 Astra generates finished UIs from one prompt and operates software by reading the screen. See how it works, benchmark scores, pricing, and limits.
Compare Ling-3.0-flash-VL with Gemini for internal vision tools: benchmark scores, per-seat vs inference cost, data residency, and governance risks.
Claude Haiku 5.5 costs $0.10/$0.50 per million tokens. Local open-weight coding models run $0.06 to $0.55 in electricity. See the real cost math.
AI earned IMO gold and disproved an 80-year-old Erdős conjecture, yet still fails simple math tweaks. See where AI math works and where it breaks.
Compare GPT-6 Astra, Claude Opus 5.5, and open-weight models like DeepSeek V4.1 Flash for internal tools, with pricing, benchmarks, and self-hosting costs.
How ChatGPT and Perplexity pick citations: query rewriting, crawler-fed retrieval, ranking by authority and freshness, and why only some pages get cited.
Compare dense, MoE, reasoning, and state-space AI models for internal tools by cost per task, latency, self-hosting hardware needs, and governance.
"ChatGPT dots" means two different OpenAI features: a camera Scan menu for PDFs and a separate always-on agent. Here's how each works and where each falls short.
OpenAI's Dots turn ChatGPT into a standing, always-on agent with its own cloud computer. Here's how it works, what it can't do alone, and what it costs.
Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.
Claude Opus 5.5 wins on raw coding capability. GPT-6 Sol wins on cost per task. Here's what that means for building internal tools instead of buying SaaS.
Opus 5.5 and GPT-6 Sol launched 90 minutes apart with mismatched benchmarks. Here's what the numbers say about building internal apps with each.
GPT-6 Astra ties Fable 5.1 on coding benchmarks at a fraction of the cost, but Fable 5.1 wins on cache economics and long runs. Here's how to pick.
GPT-6 Astra costs 2.5x more than its predecessor and still isn't private by default. Here's the real cost and privacy math against local coding models.
Same list price, different bills. Compare GPT-6 Astra and Claude Fable 5.1 on cost-per-task, cache pricing, and benchmarks for internal AI agents.
LLMs predict the next word, but that training objective builds real internal reasoning. Here's the proof, including a 13M-line Lean proof of Fermat's Last Theorem.
Fable 5.1 is shipped, priced, and API-ready for internal agents. Astra has no release date. Here's what that means for teams building agent workflows now.