R1 0528 vs Llama 4 Maverick

Overview — in our 12-test suite R1 0528 wins 10 tests, ties 2, and Llama 4 Maverick wins none. Test-by-test highlights (scores are from our testing unless otherwise noted):

• Tool calling: R1 0528 scored 5/5 (tied for 1st of 54); Llama 4 Maverick’s tool_calling run hit a transient 429 rate limit on OpenRouter during our test, so it did not register a comparable score. This means R1 handles function selection, arguments and sequencing more reliably in our agentic tool-calling tasks.
• Faithfulness: R1 5 vs Maverick 4; R1 is tied for 1st (rank 1 of 55) while Maverick ranks 34 of 55. Expect fewer source-hallucinations from R1 on tasks requiring strict adherence to source material.
• Long context: R1 5 vs Maverick 4; R1 tied for 1st (rank 1 of 55) despite Maverick’s larger raw context window (1,048,576 tokens). In practice, R1 retrieved and reasoned over 30k+ token contexts more accurately in our tests.
• Agentic planning: R1 5 vs Maverick 3; R1 tied for 1st (rank 1 of 54) while Maverick ranks 42 of 54 — R1 decomposes goals and recovers from failures better in our planning tasks.
• Multilingual & Persona consistency: R1 5 vs Maverick 4 (multilingual) and both 5 (persona). R1 ties for 1st on multilingual and persona_consistency; Maverick holds persona parity but scores lower on multilingual overall.
• Classification & Structured output: R1 4 vs Maverick 3 for classification; R1 tied for 1st (classification rank 1 of 53). Structured_output is tied (both 4; both rank 26 of 54) — both models handle JSON/schema adherence similarly in our tests.
• Safety calibration: R1 4 vs Maverick 2 (R1 rank 6 of 55; Maverick rank 12) — R1 better balances refusals and permissive answers in our safety test.
• Creative problem solving & Constrained rewriting: R1 4 vs Maverick 3 on both — R1 ranks notably higher (creative_problem_solving rank 9 vs 30; constrained_rewriting rank 6 vs 31), so we observed more feasible, non-obvious solutions and better tight-limit rewriting from R1.
• Strategic analysis: R1 4 vs Maverick 2 (R1 rank 27 of 54; Maverick rank 44) — R1 performed better at nuanced tradeoff reasoning.
• External math benchmarks (Epoch AI): Beyond our internal scores, R1 0528 scores 96.6% on MATH Level 5 (Epoch AI) and 66.4% on AIME 2025 (Epoch AI); Llama 4 Maverick has no external math scores in the payload. These external results support R1’s strong mathematical reasoning on harder problems in our testing. Caveats — R1 has operational quirks: it returns empty responses on structured_output, constrained_rewriting and agentic_planning in some cases and uses reasoning tokens that consume output budget on short tasks; it also requires high max completion tokens (min_max_completion_tokens ≈ 1000). Llama 4 Maverick is multimodal (text+image→text), supports a 1,048,576 token raw context window and a 16,384 max output token cap, and is materially cheaper per-token.

Pricing difference (per 1,000 tokens): R1 0528 — input $0.50, output $2.15; Llama 4 Maverick — input $0.15, output $0.60 (price ratio ≈ 3.58×). Practical costs if you pay for 1M input tokens + 1M output tokens (1M=1,000 mtok units): R1 0528 = $500 (input) + $2,150 (output) = $2,650. Llama 4 Maverick = $150 + $600 = $750. At 10M/10M tokens: R1 = $26,500 vs Llama = $7,500. At 100M/100M tokens: R1 = $265,000 vs Llama = $75,000. Who should care: startups, consumer apps and high-volume APIs will see large monthly differences — Llama 4 Maverick reduces token spend by roughly 72% on these examples. Enterprises that prioritize top-tier tool calling, safety calibration and long-context accuracy may accept R1’s higher spend; cost-sensitive products should prefer Llama 4 Maverick.

Choose R1 0528 if: you need top performance on agentic workflows, reliable tool calling, long-context retrieval and faithfulness — and your product can absorb higher per-token costs and accommodate R1’s quirks (empty-on-structured_output, reasoning-token accounting, high min completion sizes). Choose Llama 4 Maverick if: you need multimodal inputs (text+image), massive raw context windows, and dramatically lower token spend (R1 output $2.15/1k vs Maverick $0.60/1k); it’s the better choice for high-volume, cost-sensitive applications or prototypes where multimodality and scale matter more than winning every benchmark.