R1 0528 vs Grok 4.20

We tested across 12 benchmarks. Summary of wins: R1 0528 wins safety_calibration (score 4 vs 1) and agentic_planning (5 vs 4); Grok 4.20 wins structured_output (5 vs 4) and strategic_analysis (5 vs 4); the remaining 8 tests tie. Detailed context and what it means:

• safety_calibration: R1 0528 = 4, Grok 4.20 = 1. In our testing R1 refuses harmful requests or differentiates legitimate asks much better (R1 ranks 6 of 55; Grok ranks 32 of 55). This matters for customer-facing assistants and regulated workflows.

• agentic_planning: R1 0528 = 5, Grok 4.20 = 4. R1 is tied for 1st (one of models best at goal decomposition/failure recovery), so pick R1 when you need reliable multi-step plans and retries.

• structured_output: R1 0528 = 4, Grok 4.20 = 5. Grok ranks tied for 1st (structured_output) — it follows JSON/schema constraints more reliably in our tests, which matters for APIs, code generation with strict format, and downstream parsers.

• strategic_analysis: R1 0528 = 4, Grok 4.20 = 5. Grok ranks tied for 1st on nuanced tradeoff reasoning; choose it for reports, nuanced numeric tradeoffs, or strategy synthesis.

• tool_calling: tie at 5/5. Both choose functions, arguments, and sequencing correctly in our tests (each tied for top rank on tool_calling), so either model can drive agentic tool workflows on correctness.

• faithfulness, classification, long_context, persona_consistency, multilingual, constrained_rewriting, creative_problem_solving: ties or near-ties. Notably R1 scores 5 on faithfulness/long_context/persona_consistency and ties for 1st in those ranks; Grok also ties for 1st on long_context, persona_consistency and faithfulness.

• external math benchmarks (Epoch AI): R1 0528 posts 96.6% on math_level_5 and 66.4% on aime_2025 in our payload (these are Epoch AI scores). Grok has no math_level_5/aime_2025 entries in the provided data, so R1 has a clear, attributed edge on those external math measures in our dataset.

Practical takeaway: R1 is safer and better at planning in our tests; Grok is stronger at strict schema output and strategic numeric reasoning. Both are top-tier on tool calling and long-context handling per our rankings.

Pricing per 1,000 tokens (mTok) assuming a 1:1 input:output token split: R1 0528 charges $0.50 input + $2.15 output = $2.65 per mTok. Grok 4.20 charges $2.00 input + $6.00 output = $8.00 per mTok. At 1M tokens/mo (1,000 mTok): R1 ≈ $2,650; Grok ≈ $8,000. At 10M tokens/mo: R1 ≈ $26,500; Grok ≈ $80,000. At 100M tokens/mo: R1 ≈ $265,000; Grok ≈ $800,000. The output-cost gap (Grok is ~3.02x more expensive per mTok) matters most for high-volume services (SaaS chat, search, large-scale inference). Small teams or low-traffic prototypes will feel the difference less, but any sustained production workload should run cost projections with these per-mTok rates.

Choose R1 0528 if: you need safer assistant behavior (safety_calibration 4 vs 1), stronger agentic planning (5 vs 4), lower cost (≈$2.65/mTok assumed 1:1 I/O), or large-context text-only workflows. Specific use cases: customer-facing chatbots, safety-sensitive agents, multilingual conversational services, and high-volume text-only inference.

Choose Grok 4.20 if: your priority is strict JSON/schema compliance (structured_output 5 vs 4), top-ranked strategic analysis (5 vs 4), multimodal inputs (files/images to text) or extremely large context windows (2,000,000 tokens vs R1's 163,840). Specific use cases: automated data pipelines requiring exact schema, decision-support reports, multimodal apps, or one-off high-value tasks where cost is secondary.