Guides for this category are in preparation.
Continuous and verifiable tracking of AI performance. Data synthesizes independent online benchmarks (LMSYS Chatbot Arena, Hugging Face Open LLM Leaderboard, SWE-bench Verified, and Artificial Analysis). Editorial product rankings with deterministic scoring are currently in preparation.
Testing and tracking frontier models from Anthropic (Claude), OpenAI (GPT & o-series), Google (Gemini), and xAI (Grok). Verifying technical specs, throughput, and logic accuracy prior to scoring.
Tracking open weights (DeepSeek R1/V3, Llama 3.3, Qwen 2.5 Coder, Mistral) and local inference runtimes (Ollama, vLLM, llama.cpp). Analysis of hardware requirements, minimum VRAM, quantization, and data privacy.
Aggregated from blind crowd-sourced tests (Arena Elo), real software bug resolution (SWE-bench), and latency/speed measurements.
| Model | Type | Arena Elo (LMSYS) | SWE-bench (Coding) | MMLU-Pro (Reasoning) | Speed | Context | Cost / License |
|---|---|---|---|---|---|---|---|
DeepSeek R1DeepSeek | Open Weights | 1334 | 49.2% | 84.0% | ~40 tok/s | 128ktok | Open Source MIT / $0.55 API |
OpenAI o1 / o3-miniOpenAI | Frontier API | 1342 | 48.9% | 83.3% | ~45 tok/s | 200ktok | $1.10 - $15.00 / 1M |
Claude 3.5 SonnetAnthropic | Frontier API | 1283 | 49.0% | 78.0% | ~80 tok/s | 200ktok | $3.00 / $15.00 / 1M |
Gemini 2.0 Flash / ProGoogle DeepMind | Frontier API | 1302 | 42.1% | 75.4% | ~160 tok/s | 1M - 2Mtok | $0.10 / $0.40 / 1M |
DeepSeek V3DeepSeek | Open Weights | 1290 | 41.5% | 75.9% | ~65 tok/s | 128ktok | Open Source MIT / $0.14 API |
GPT-4oOpenAI | Frontier API | 1286 | 38.8% | 72.5% | ~110 tok/s | 128ktok | $2.50 / $10.00 / 1M |
Qwen 2.5 Coder 32BAlibaba / Qwen | Open Weights | 1245 | 46.5% | 65.2% | ~55 tok/s | 128ktok | Open Source Apache 2.0 |
Llama 3.3 70B InstructMeta AI | Open Weights | 1265 | 36.2% | 68.9% | ~40 tok/s | 128ktok | Llama 3.3 Community |
Guide to understanding AI architectures and benchmark metrics.
The 671B Mixture-of-Experts architecture activates only 37B parameters per token, drastically cutting compute cost. Using pure reinforcement learning without proprietary synthetic datasets, R1 demonstrated open models can match OpenAI o1 on math and algorithms.
SWE-bench Verified measures AI capability in resolving real GitHub issues (large repo comprehension, bug reproduction, writing fix patches, and passing test suites). Claude 3.5 Sonnet remains the reference standard for autonomous coding agents like Cursor, Windsurf, and Cline.
The 'o' series introduces inference-time compute: instead of immediate token output, it generates internal chain-of-thought tokens. This approach eliminates cascading hallucinations and dominates competitive math (AIME) and programming (Codeforces).
Running AI models locally guarantees zero data sharing and full GDPR compliance. With 4-bit GGUF or EXL2 quantization, Qwen 2.5 Coder 32B needs ~20-24 GB memory (runs on an RTX 3090/4090 or Apple Silicon Mac via Ollama/vLLM), exceeding previous-gen closed models.
Periodic ingestion from LMSYS Chatbot Arena, Hugging Face, SWE-bench, and official research newsrooms.
Automated transcript ingestion from AI creator and researcher channels (Matt Wolfe, Fireship, Simone Rizzo, Wes Roth) capturing hands-on tests.
Scanning feeds and trending repos to identify emerging open weights, GGUF releases, and local inference runtimes.
No hallucinated or estimated figures: final catalogue scores are calculated deterministically strictly against verified evidence.
