R1 vs Grok 4.20

We compared the two models across our 12-test suite (scores are our internal 1–5 metrics unless otherwise noted). Summary of wins: Grok 4.20 wins structured output, tool calling, classification and long context; R1 wins creative problem solving; the rest are ties. Details:

• Structured output: Grok 4.20 scores 5 vs R1's 4. Grok ranks “tied for 1st with 24 other models out of 54 tested,” while R1 ranks 26 of 54. That means Grok is more reliable for strict JSON/schema outputs and format adherence in production pipelines.

• Tool calling: Grok 4.20 scores 5 vs R1's 4. Grok’s tool calling rank is “tied for 1st with 16 other models out of 54,” R1 is rank 18 of 54. In practice Grok is more likely to pick the right function, sequence calls correctly, and produce accurate arguments.

• Classification: Grok 4.20 scores 4 vs R1's 2. Grok is “tied for 1st with 29 other models out of 53,” while R1 is rank 51 of 53. For routing, labeling, or intent detection, Grok is the clear choice.

• Long context: Grok 4.20 scores 5 vs R1's 4. Grok is “tied for 1st with 36 other models out of 55,” whereas R1 is rank 38 of 55. Grok will better preserve retrieval accuracy over 30K+ token prompts.

• Creative problem solving: R1 scores 5 vs Grok’s 4; R1 is tied for 1st (with 7 others) in this test while Grok ranks 9 of 54. Expect R1 to produce more non‑obvious, feasible ideas and brainstorming outputs.

• Ties: strategic analysis (both 5), constrained rewriting (both 4), faithfulness (both 5), safety calibration (both 1), persona consistency (both 5), agentic planning (both 4), multilingual (both 5). For these areas the models are comparable by our tests.

• External math benchmarks (Epoch AI): R1 posts 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI). Grok 4.20 has no math-level external scores in the payload. Those R1 scores indicate strong performance on advanced math tests in our dataset.

In short: Grok 4.20 dominates where determinism, tooling, and long-context fidelity matter; R1 is stronger for creative ideation and advanced math in our tests.

R1 input/output costs: $0.7 / $2.5 per million tokens. Grok 4.20 input/output: $2 / $6 per million tokens. Assuming a 50/50 input/output token split, cost per 1M tokens is $1.60 for R1 vs $4.00 for Grok 4.20. At scale that means roughly: 1M tokens — R1 $1.60 vs Grok $4.00; 10M — R1 $16 vs Grok $40; 100M — R1 $160 vs Grok $400. Teams running large-volume inference (10M+ tokens) will see meaningful savings with R1; latency- or tool-heavy production apps that need Grok’s strengths should budget roughly 2.5x higher per-token spend.

Choose R1 if: you need a low-cost model (≈$1.60 per 1M tokens with a 50/50 split), prioritize creative problem solving (5 vs 4) or advanced math (MATH Level 5 93.1%, AIME 2025 53.3% — Epoch AI), or want a model with a 64k context and explicit reasoning tokens.

Choose Grok 4.20 if: you need robust tool calling, strict structured output (JSON/schema), high classification accuracy, or the strongest long‑context behavior — and you can accept higher per‑token costs (≈$4.00 per 1M tokens with a 50/50 split).