Llama 4 Maverick vs o4 Mini

Summary of our 12-test suite (scores from the payload):

• Tool calling: o4 Mini 5 vs Llama 4 Maverick (no score reported; payload notes transient rate-limiting). o4 Mini wins and ranks tied for 1st of 54 models on tool calling in our rankings — important for function selection and accurate argument sequencing.
• Long context: o4 Mini 5 vs Llama 4 Maverick 4. o4 Mini is tied for 1st of 55 models on long context; expect better retrieval and accuracy across 30K+ token contexts.
• Structured output: o4 Mini 5 vs Llama 4 Maverick 4. o4 Mini ties for 1st of 54 models; this matters for strict JSON/schema adherence.
• Strategic analysis: o4 Mini 5 vs Llama 4 Maverick 2. o4 Mini ties for 1st of 54 models — better nuanced tradeoff reasoning with numbers.
• Creative problem solving: o4 Mini 4 vs Llama 4 Maverick 3. o4 Mini ranks 9th of 54, so it produces more specific feasible ideas in our tests.
• Classification: o4 Mini 4 vs Llama 4 Maverick 3. o4 Mini ties for 1st of 53 — better routing and categorization accuracy.
• Agentic planning: o4 Mini 4 vs Llama 4 Maverick 3. o4 Mini ranks 16th of 54, showing stronger goal decomposition and recovery.
• Faithfulness: o4 Mini 5 vs Llama 4 Maverick 4. o4 Mini ties for 1st of 55 — it sticks to source material more reliably in our tests.
• Multilingual: o4 Mini 5 vs Llama 4 Maverick 4. o4 Mini ties for 1st of 55 models — better non-English parity.
• Safety calibration: Llama 4 Maverick 2 vs o4 Mini 1. Llama wins this test in our suite (rank 12 of 55 vs o4 Mini rank 32), meaning Llama is more likely to correctly refuse harmful requests while permitting legitimate ones in our testing.
• Constrained rewriting and persona consistency: ties (both models scored 3 on constrained rewriting and 5 on persona consistency). Persona consistency ties are both tied for 1st among many models. External benchmarks (Epoch AI): o4 Mini scores 97.8% on MATH Level 5 and 81.7% on AIME 2025 according to Epoch AI; Llama 4 Maverick has no external scores in the payload. These external math scores corroborate o4 Mini’s strong reasoning/math capability in our view. Practical meaning: o4 Mini consistently outscored Llama 4 Maverick across core developer-facing tasks (tool calling, structured output, long-context retrieval, faithfulness, classification, strategic analysis). Llama’s single win on safety calibration is relevant for sensitive deployments and moderation-focused agents, and its massive context_window (1,048,576 tokens) and far-lower costs make it attractive for budget-bound or very-high-context workflows. Note that Llama 4 Maverick reported a transient tool calling rate limit during one test run (429 on OpenRouter); that is noted in the payload as likely transient.

Per the payload, Llama 4 Maverick charges $0.15 input / $0.60 output per mTok; o4 Mini charges $1.10 input / $4.40 output per mTok. Assuming a 50/50 split between input and output tokens (explicit assumption):

• 1M tokens (500 mTok input + 500 mTok output): Llama 4 Maverick = $0.15500 + $0.60500 = $75 + $300 = $375; o4 Mini = $1.10500 + $4.40500 = $550 + $2,200 = $2,750.
• 10M tokens: multiply by 10 → Llama = $3,750; o4 Mini = $27,500.
• 100M tokens: Llama = $37,500; o4 Mini = $275,000. Who should care: any high-volume product (chatbots, agent fleets, batch processing at millions of tokens/month) will see a ~7.33× absolute cost gap per token and an 86% lower spend with Llama 4 Maverick under this split (price ratio ~0.1364 as provided). Teams that need top-tier tool use, structured outputs, or best-in-class faithfulness may accept o4 Mini's higher price; cost-sensitive deployments and experimentation stacks should prefer Llama 4 Maverick.

Choose o4 Mini if you need the best performance for tool calling, long-context retrieval, structured outputs, classification, faithfulness and strategic reasoning — it wins 9 of 12 benchmarks in our tests and ties for 1st on several key categories, and posts strong external math scores (MATH Level 5 97.8% and AIME 2025 81.7% per Epoch AI). Choose Llama 4 Maverick if budget or massive context windows are critical: it costs roughly $375 vs $2,750 per 1M tokens (50/50 I/O example), wins safety calibration in our testing, and offers a 1,048,576-token context window suited to extremely long documents or archival retrieval at scale.