GPT-4o-mini vs Llama 4 Maverick

Across our 12-test suite the two models split wins 3–3 with six ties. In our testing GPT-4o-mini wins: tool calling (score 4 — ranks 18 of 54, tied with 28 others), classification (score 4 — tied for 1st with 29 other models out of 53), and safety calibration (score 4 — rank 6 of 55). Those results mean GPT-4o-mini is more reliable for function selection, argument accuracy, routing, and refusals vs. harmful requests. Llama 4 Maverick wins creative problem solving (3 vs GPT-4o-mini’s 2; rank 30 vs 47), faithfulness (4 vs 3; rank 34 vs 52), and persona consistency (5 vs 4; tied for 1st with 36 others vs GPT-4o-mini rank 38). That indicates 4 Maverick produces more non-obvious, feasible ideas and holds role/character better while sticking to source material. They tie on structured output (both 4, rank 26 of 54), strategic analysis (both 2, rank 44 of 54), constrained rewriting (both 3, rank 31 of 53), long context (both 4, rank 38 of 55), agentic planning (both 3, rank 42 of 54), and multilingual (both 4, rank 36 of 55) — so neither has a clear edge on long-context retrieval at 30K+ tokens or on multilingual parity in our tests. Additional external math signals for GPT-4o-mini in our payload show lower math performance: MATH Level 5 52.6% (rank 13 of 14) and AIME 2025 6.9% (rank 21 of 23) — these are external benchmarks (Epoch AI) referenced in the payload and suggest neither model is a top performer on advanced competition math in our dataset. Note: Llama 4 Maverick’s tool calling test hit a transient 429 on OpenRouter during testing (payload quirk), which likely impacted that specific measurement.

Both models use identical rates in the payload: input $0.15 per mTok and output $0.60 per mTok. At 1M tokens/month (1,000 mTok) that’s $150 input and $600 output — $750 total. At 10M tokens/month: $1,500 input, $6,000 output — $7,500 total. At 100M tokens/month: $15,000 input, $60,000 output — $75,000 total. Because priceRatio = 1 and per-mTok rates match, the cost differential is zero; high-volume apps (10M–100M tokens) should focus on which model’s capability profile matches their needs rather than cost savings between these two models.

Choose GPT-4o-mini if you need reliable tool calling, high-accuracy classification/routing, and stronger safety calibration in production integrations (it scores 4 on tool calling and safety calibration and ties for 1st in classification in our tests). Choose Llama 4 Maverick if you prioritize creative problem generation, faithfulness to source material, and strong persona consistency (it scores 3 on creative problem solving vs 2, 4 on faithfulness vs 3, and 5 on persona consistency), or if you need the massive 1,048,576-token context window for extremely long documents. Because both models have identical per-mTok pricing in the payload, make the decision based on these capability trade-offs rather than cost.