GPT-5.2 vs Llama 4 Maverick

Overview: In our 12-test suite GPT-5.2 wins 10 tests, Llama 4 Maverick wins 0, and the two tie on 2 tests (structured output and persona consistency). Below are test-by-test outcomes with context and what they mean for tasks. Strategic analysis (tradeoffs): GPT-5.2 scores 5 vs Llama 4 Maverick 2 — GPT-5.2 is tied for 1st of 54 models (tied with 25 others), so it handles nuanced tradeoffs and numeric reasoning best in our testing. Structured output (JSON/schema): both score 4 and are tied (rank 26 of 54), meaning both models meet schema/format constraints comparably. Persona consistency: both score 5 and are tied for 1st of 53, so both preserve character and resist injection equally in our tests. Agentic planning: GPT-5.2 5 vs Llama 4 Maverick 3 — GPT-5.2 tied for 1st (14 others) meaning better goal decomposition and failure recovery. Constrained rewriting: GPT-5.2 4 vs Llama 4 Maverick 3 — GPT-5.2 ranks 6 of 53, so it compresses/rewrites within tight limits more reliably. Creative problem solving: GPT-5.2 5 vs Llama 4 Maverick 3 — GPT-5.2 tied for top tier, producing more non-obvious feasible ideas in our tests. Tool calling: GPT-5.2 4 (rank 18 of 54); Llama 4 Maverick’s tool calling test hit a 429 rate limit on OpenRouter (quark noted in payload) and was rate-limited during testing, so results are not comparable—GPT-5.2 demonstrates reliable function selection and sequencing in our run. Faithfulness: GPT-5.2 5 vs Llama 4 Maverick 4 — GPT-5.2 ties for 1st of 55, indicating stronger adherence to source material and fewer hallucinations in our tests. Classification: GPT-5.2 4 vs Llama 4 Maverick 3 — GPT-5.2 tied for 1st (29 others) so routing/categorization is stronger. Long context: GPT-5.2 5 vs Llama 4 Maverick 4 — GPT-5.2 tied for 1st of 55 in our long-context retrieval tests even though Llama 4 Maverick’s raw context window is larger (1,048,576 vs GPT-5.2’s 400,000). Safety calibration: GPT-5.2 5 (tied for 1st of 55) vs Llama 4 Maverick 2 (rank 12 of 55) — GPT-5.2 better distinguishes harmful vs legitimate content per our safety test. Multilingual: GPT-5.2 5 vs Llama 4 Maverick 4 — GPT-5.2 ranks much higher in our multilingual tasks. External benchmarks (supplementary): Beyond our internal scores, GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI) — rank 5 of 12 — and 96.1% on AIME 2025 (Epoch AI) — rank 1 of 23. Llama 4 Maverick has no SWE-bench or AIME scores in the payload. Practical meaning: pick GPT-5.2 for high-stakes reasoning, faithful summarization, multi-step agentic flows and stricter safety needs; pick Llama 4 Maverick when budget and raw context window are the primary constraints (noting its tool calling test was rate-limited in our run).

Costs are materially different. Per the payload, GPT-5.2 charges $1.75 per mTok input and $14 per mTok output; Llama 4 Maverick charges $0.15 input and $0.60 output (price ratio ≈ 23.33x). Assuming a 50/50 split of input/output tokens: • 1M tokens (500k input + 500k output) → GPT-5.2 ≈ $7,875; Llama 4 Maverick ≈ $375. • 10M tokens → GPT-5.2 ≈ $78,750; Llama 4 Maverick ≈ $3,750. • 100M tokens → GPT-5.2 ≈ $787,500; Llama 4 Maverick ≈ $37,500. If your usage is output-heavy the gap widens because GPT-5.2’s output rate is $14/mTok. Teams with millions of monthly tokens (SaaS, search, chat fleets) should care deeply about this gap; small-scale or high-value, high-accuracy use cases may justify GPT-5.2’s cost.

Choose GPT-5.2 if you need top-tier reasoning, faithfulness, safety and long-context performance in production — e.g., multi-step agents, legal/medical summarization, complex decision support, or high-value research where mistakes are costly. Choose Llama 4 Maverick if your priority is minimizing runtime cost or you require the largest raw context window for bulk ingestion (e.g., very high-volume indexing or archival processing) and you can accept lower scores on strategic analysis, safety calibration and faithfulness. Note: Llama 4 Maverick’s tool calling test was rate-limited in our testing, so validate tool workflows before committing.