GPT-4o vs Llama 4 Maverick

Overview (our 12-test suite): GPT-4o wins three categories (tool calling, classification, agentic planning), Llama 4 Maverick wins one (safety calibration), and eight categories tie. Detailed walk-through: - Tool calling: GPT-4o scored 4 on our test and ranks 18 of 54 (display: "rank 18 of 54 (29 models share this score)"); Llama 4 Maverick's tool calling run hit a transient 429 rate limit on OpenRouter and was marked rate_limited, so GPT-4o is the practical winner for function selection, argument accuracy, and sequencing. - Classification: GPT-4o scores 4 vs Maverick 3; GPT-4o is tied for 1st in classification ("tied for 1st with 29 other models out of 53 tested"), so it is better for routing, tagging, and intent detection in our tests. - Agentic planning: GPT-4o 4 vs Maverick 3; GPT-4o ranks 16 of 54 ("rank 16 of 54 (26 models share this score)"), meaning it decomposes goals and recovery paths better in our scenarios. - Safety calibration: Llama 4 Maverick wins (score 2 vs GPT-4o 1); Maverick ranks 12 of 55 here ("rank 12 of 55 (20 models share this score)"), so it more reliably refuses harmful prompts while permitting legitimate requests in our testing. - Ties: structured output (4/4), strategic analysis (2/2), constrained rewriting (3/3), creative problem solving (3/3), faithfulness (4/4), long context (4/4), persona consistency (5/5), multilingual (4/4). For example, both models scored 4 on long context and rank 38 of 55 (GPT-4o long context rank 38 of 55, tied) — both handle 30K+ retrieval tasks equivalently in our tests. - External benchmarks (Epoch AI): GPT-4o posts SWE-bench Verified 31%, MATH Level 5 53.3%, and AIME_2025 6.4% (these external scores are from Epoch AI and supplementary to our internal scores). What this means for real tasks: choose GPT-4o when you need dependable tool calling, high-quality classification, and better agentic planning behavior; choose Llama 4 Maverick when safety calibration and cost per token are decisive. Remember many categories tie, so feature and cost trade-offs often determine the practical winner.

Payload prices: GPT-4o input $2.50/mtok and output $10/mtok; Llama 4 Maverick input $0.15/mtok and output $0.60/mtok. Treating mtok as per‑1,000 tokens, output-only monthly costs scale to: GPT-4o = $10,000 (1M tokens), $100,000 (10M), $1,000,000 (100M); Maverick = $600 (1M), $6,000 (10M), $60,000 (100M). Input costs scale similarly (GPT-4o $2,500/1M vs Maverick $150/1M). The models' output-price ratio is 16.67x (priceRatio). Who should care: startups, SaaS products, or any service with tens of millions of tokens/month will see six-figure or higher differences; teams with tight margins or very high throughput should prefer Llama 4 Maverick for cost-efficiency, while teams prioritizing tool integration and developer convenience may accept GPT-4o's premium.

Choose GPT-4o if you need best-in-class tool calling, strong classification, and agentic planning in our tests and can accept a substantial price premium (output $10/mtok). Specific use cases: developer tools with external API calls, automated task planners, or apps where function selection and argument accuracy matter. Choose Llama 4 Maverick if per-token cost and safety calibration matter more: it costs $0.60/output-mtok, wins safety calibration in our tests, and offers a far larger context window (1,048,576 vs GPT-4o 128,000), making it better for large-context, multimodal, and high-volume deployments on a budget.