R1 vs Gemma 4 26B A4B

Summary of our 12-test suite (scores are our 1–5 internal grades and ranks are from the payload): • Gemma wins (bWins): structured_output (Gemma 5 vs R1 4; Gemma tied for 1st of 54), tool_calling (Gemma 5 vs R1 4; Gemma tied for 1st of 54), classification (Gemma 4 vs R1 2; Gemma tied for 1st of 53, R1 rank 51 of 53), long_context (Gemma 5 vs R1 4; Gemma tied for 1st of 55, R1 rank 38 of 55). These translate to real benefits: Gemma is more reliable producing JSON/strict formats, better at choosing and sequencing function calls, and superior with documents >30K tokens. • R1 wins (aWins): constrained_rewriting (R1 4 vs Gemma 3; R1 rank 6 of 53) and creative_problem_solving (R1 5 vs Gemma 4; R1 tied for 1st). That means R1 is stronger for tight-character compressions and generating high-variance creative ideas under constraints. • Ties (ties): strategic_analysis (both 5), faithfulness (both 5), safety_calibration (both 1), persona_consistency (both 5), agentic_planning (both 4), multilingual (both 5). Note safety_calibration is weak for both (score 1) per our tests. • External math benchmarks (supplementary): R1 posts 93.1% on math_level_5 and 53.3% on aime_2025 — these are external measures from Epoch AI and indicate R1’s strong math performance on those specific benchmarks; Gemma has no external math scores in the payload. • Rankings context: several of Gemma’s wins are top-tier across our tested models (tied-for-1st in long_context, structured_output, tool_calling), while R1’s creative and constrained-writing wins place it in the higher ranks for those exact tasks (e.g., constrained_rewriting rank 6). In practice: pick Gemma when you need reliable function-calling, strict output formats, or very long-context work; pick R1 when you must compress content tightly or prioritize highest-scoring creative/math outputs and can accept much higher token costs.

Pricing difference (per payload): R1 input $0.70/mTok and output $2.50/mTok; Gemma input $0.08/mTok and output $0.35/mTok. Assuming a 50/50 input:output split, per 1M tokens R1 ≈ $1,600 (input $700 + output $2,500 scaled to 50/50 → $350+$1,250) and Gemma ≈ $215 (input $80 + output $350 scaled → $40+$175). At 10M and 100M tokens/month the 50/50 totals are roughly: R1 $16,000 / $160,000; Gemma $2,150 / $21,500. The payload gives a priceRatio of 7.142857, matching these examples. Who should care: high-volume deployments, multi-tenant SaaS, and cost-sensitive teams should prefer Gemma; experimental or niche workloads that need R1's strengths (creative compression or specific math-oriented workflows) must budget for materially higher token bills.

Choose Gemma 4 26B A4B if you need: • long-document understanding and retrieval (long_context score 5; tied for 1st), • reliable JSON/format compliance (structured_output 5; tied for 1st), • robust tool calling and classification (tool_calling 5, classification 4; classification tied for 1st). Choose R1 if you need: • best-in-class constrained rewriting and creative ideation (constrained_rewriting 4, creative_problem_solving 5), • stronger external math benchmark performance (math_level_5 93.1%, aime_2025 53.3% per Epoch AI), and you can accept ~7.14× higher per-token costs.