Llama 4 Maverick vs o3

o3 wins 8 of 12 benchmarks in our testing; Llama 4 Maverick wins 1; 3 are tied.

Where o3 leads:

• Strategic analysis: o3 scores 5/5 (tied for 1st of 54 models with 25 others) vs Maverick's 2/5 (rank 44 of 54). This is the widest gap in the entire comparison — a 3-point difference in nuanced tradeoff reasoning. For business analysis, investment memos, or complex decision support, this gap is material.
• Agentic planning: o3 scores 5/5 (tied for 1st of 54) vs Maverick's 3/5 (rank 42 of 54). A 2-point gap in goal decomposition and failure recovery — critical for multi-step autonomous workflows.
• Tool calling: o3 scores 5/5 (tied for 1st of 54 with 16 others) vs Maverick's score, which was rate-limited during our testing on 2026-04-13 and could not be recorded. Maverick's tool calling score is absent from our results; treat any tool-calling comparison here as incomplete.
• Faithfulness: o3 scores 5/5 (tied for 1st of 55 with 32 others) vs Maverick's 4/5 (rank 34 of 55). Both are solid, but o3's advantage matters in RAG pipelines where hallucination carries real cost.
• Structured output: o3 scores 5/5 (tied for 1st of 54 with 24 others) vs Maverick's 4/5 (rank 26 of 54). For JSON schema compliance in production APIs, o3 is more reliable.
• Multilingual: o3 scores 5/5 (tied for 1st of 55 with 34 others) vs Maverick's 4/5 (rank 36 of 55). Both are above the median (p50 = 5), but o3 reaches the ceiling.
• Creative problem solving: o3 scores 4/5 (rank 9 of 54 with 20 others) vs Maverick's 3/5 (rank 30 of 54).
• Constrained rewriting: o3 scores 4/5 (rank 6 of 53 with 24 others) vs Maverick's 3/5 (rank 31 of 53).

Where they tie:

• Classification: Both score 3/5, both rank 31 of 53 in the same tier of 20 models.
• Long context: Both score 4/5, both rank 38 of 55. Neither model distinguishes itself here.
• Persona consistency: Both score 5/5, tied for 1st of 53 with 36 other models. Not a differentiator.

Where Llama 4 Maverick leads:

• Safety calibration: Maverick scores 2/5 (rank 12 of 55 with 19 others) vs o3's 1/5 (rank 32 of 55). This is the only benchmark Maverick wins outright, and it's notable: o3 is below the 25th percentile (p25 = 1) on safety calibration in our testing, meaning it's among the least calibrated models we've tested on refusing harmful requests while permitting legitimate ones.

External benchmarks (Epoch AI): o3 scores 62.3% on SWE-bench Verified (rank 9 of 12 models with this data), 97.8% on MATH Level 5 (rank 2 of 14, tied with 2 others), and 83.9% on AIME 2025 (rank 12 of 23). These place o3 in the upper tier for math and competition problem-solving, though its SWE-bench Verified score sits near the median of models with that data (p50 = 70.8%). No external benchmark data is available for Llama 4 Maverick in our payload.

Llama 4 Maverick costs $0.15/M input tokens and $0.60/M output tokens. o3 costs $2.00/M input and $8.00/M output — roughly 13x more expensive on output. In practice: at 1M output tokens/month, you pay $0.60 vs $8.00, a $7.40 difference that barely registers. At 10M output tokens/month, that gap widens to $74. At 100M output tokens/month, you're looking at $60,000 versus $800,000 — a $740,000 annual difference that fundamentally changes the business case. The pricing gap matters most to developers running high-volume pipelines: content generation at scale, document processing, chatbot infrastructure. For low-to-medium volume use cases (under 10M tokens/month), the quality gains from o3 likely justify the cost. Above that threshold, the question becomes whether o3's benchmark advantages translate directly into business value that offsets a near-13x cost multiplier.

Choose o3 if: You're building agentic workflows, complex tool-calling pipelines, or applications requiring strong structured output reliability. o3's 5/5 scores in agentic planning, tool calling, structured output, strategic analysis, faithfulness, multilingual, and persona consistency make it the stronger general-purpose choice for developers building production systems. Its math performance — 97.8% on MATH Level 5 and 83.9% on AIME 2025 (Epoch AI) — also makes it the clear pick for quantitative reasoning tasks. Budget: expect to pay $8.00/M output tokens.

Choose Llama 4 Maverick if: You're running high-volume pipelines where $8.00/M output tokens is unsustainable, and your use cases don't heavily depend on agentic planning or strategic analysis. At $0.60/M output tokens, it's 13x cheaper, scores competitively on persona consistency (5/5), faithfulness (4/5), and long context (4/5), and actually outperforms o3 on safety calibration in our testing. It also accepts image inputs and has a 1M token context window. Developers processing millions of documents, running bulk classification, or building chatbots with softer quality requirements will find Maverick's cost profile compelling. Note that Maverick's tool calling results were unavailable in our testing due to a rate limit, so validate that capability independently before deploying in tool-heavy workflows.