Benchmarks lie. Deployments don’t.
Everything I write about models — single-model reviews, performance notes from real workloads, and head-to-head matchups. Scored on what matters in production, updated as models ship.
| Model family | Strongest at | Long context | Cost tier | My one-line verdict |
|---|---|---|---|---|
| Claude | Complex reasoning, code, agentic work | Excellent | $$$ | The default for high-stakes reasoning and agents. |
| GPT | Breadth, ecosystem, multimodal | Very good | $$$ | Widest tooling surface; strong generalist. |
| Gemini | Native multimodal, huge context | Excellent | $$ | Best when video/context volume is the problem. |
| Llama | Open weights, on-prem control | Good | $ | The compliance-friendly self-host answer. |
| DeepSeek / Mistral | Cost-efficient reasoning | Good | $ | Where the price-performance frontier lives. |
Qualitative tiers from my own deployment evals, not vendor benchmarks. Snapshot: July 2026.
Matchup
Claude vs GPT for enterprise RAG
Same corpus, same retriever, 400 real support queries. Where each model hallucinates, and why grounding instructions matter more than the model choice past a threshold.
187 hearts
Review
Open weights vs API for regulated industries
Llama on your own metal against frontier APIs: the real TCO math, the compliance wins that are actually wins, and the capability tax you pay for control.
143 hearts
Performance note
Small models for agent routing
Does your orchestrator need a frontier model? I routed 10k agent tasks through models a tenth the price. The answer is mostly no — with two sharp exceptions.
96 hearts