R1 vs Llama 4 Maverick

Overview: in our 12-test suite R1 wins 7 tests, Llama 4 Maverick wins 2, and 3 tests are ties (see win/loss summary in payload). Detailed walk-through (scores and ranks are from our testing):

• Strategic analysis: R1 5 vs Llama 4 Maverick 2 — R1 tied for 1st on this test ("tied for 1st with 25 other models out of 54 tested"); expect stronger numeric tradeoff reasoning with R1.
• Constrained rewriting: R1 4 vs Llama 4 Maverick 3 — R1 ranks 6 of 53 (display: "rank 6 of 53 (25 models share this score)") meaning better at hard compression/limits.
• Creative problem solving: R1 5 vs Llama 4 Maverick 3 — R1 tied for 1st ("tied for 1st with 7 other models"), so it produces more non-obvious, feasible ideas in our tests.
• Tool calling: R1 4; Llama 4 Maverick’s tool_calling hit a transient 429 rate limit on OpenRouter during our test (payload notes a rate limit). We record R1 as the winner here; R1’s tool_calling rank is 18 of 54 ("rank 18 of 54 (29 models share this score)") indicating reliable function selection and argument accuracy in our runs.
• Faithfulness: R1 5 vs Llama 4 Maverick 4 — R1 tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55"), so it better sticks to source material in our evaluation.
• Agentic planning: R1 4 vs Llama 4 Maverick 3 — R1 ranks 16 of 54, showing stronger decomposition and failure recovery in our tests.
• Multilingual: R1 5 vs Llama 4 Maverick 4 — R1 tied for 1st on multilingual quality ("tied for 1st with 34 other models out of 55 tested").
• Classification: R1 2 vs Llama 4 Maverick 3 — Llama 4 Maverick wins here (rank 31 of 53), so it is better at routing/categorization in our tests.
• Safety calibration: R1 1 vs Llama 4 Maverick 2 — Llama 4 Maverick ranks better ("rank 12 of 55"), meaning it refused harmful prompts more accurately in our suite.
• Ties: structured_output both 4 ("rank 26 of 54 (27 models share this score)"), long_context both 4 ("rank 38 of 55"), persona_consistency both 5 ("tied for 1st with 36 other models"). Supplementary external data: beyond our internal 1–5 scores, R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI) — we cite Epoch AI for those external benchmarks. Practical meaning: R1 is clearly stronger for multi-step reasoning, math/coding-adjacent tasks, and multilingual output; Llama 4 Maverick is materially cheaper, supports text+image->text modality and a massive 1,048,576 context window, and is safer in our safety calibration test.

Costs shown are per 1,000 tokens (mTok). R1 input/output = $0.70 / $2.50; Llama 4 Maverick input/output = $0.15 / $0.60. Assuming a 50/50 split of input vs output tokens: at 1M tokens/month (1,000 mTok total, 500 in/500 out) R1 ≈ $1,600/month vs Llama 4 Maverick ≈ $375/month (R1 +$1,225). At 10M tokens/month R1 ≈ $16,000 vs Llama 4 Maverick ≈ $3,750 (+$12,250). At 100M tokens/month R1 ≈ $160,000 vs Llama 4 Maverick ≈ $37,500 (+$122,500). The payload gives a priceRatio of 4.1667 — R1 is roughly 4× more expensive per token. Who cares: startups and high-volume deployments where tokens dominate costs should favor Llama 4 Maverick; teams needing R1’s superior reasoning and faithfulness should budget for the premium and be aware R1 also requires large max_completion_tokens (payload notes min_max_completion_tokens 1000 and uses reasoning tokens).

Choose R1 if: you need top-tier strategic analysis, creative problem solving, faithfulness, or multilingual parity (R1 scores 5 on those tests in our runs) and can absorb ~4× higher token costs. Also pick R1 when reliable tool calling and stronger agentic planning are required. Choose Llama 4 Maverick if: you must minimize cost at scale (R1 ≈ 4× token price), need multimodal image→text inputs or the 1,048,576-token context window, and prefer better safety calibration and classification according to our tests.