R1 0528 vs Gemma 4 26B A4B

Head-to-head summary from our 12-test suite (payload scores):

• R1 0528 wins (where it outscored Gemma): • agentic_planning — R1 5 vs Gemma 4; R1 tied for 1st of 54 (tied with 14) while Gemma ranks 16 of 54 (26 share). Meaning: R1 is stronger at goal decomposition and failure recovery in our tests. Note: R1 has a quirk that reasoning tokens consume output budget and needs high max_completion_tokens. • constrained_rewriting — R1 4 vs Gemma 3; R1 ranks 6 of 53. Practical: R1 is better at tight compression and character-limit rewrites. • safety_calibration — R1 4 vs Gemma 1; R1 ranks 6 of 55 vs Gemma rank 32 of 55. R1 is substantially more reliable at refusing harmful prompts and permitting legitimate ones in our tests.
• Gemma 4 26B A4B wins: • structured_output — Gemma 5 vs R1 4; Gemma tied for 1st (tied with 24 of 54). For JSON/schema tasks, Gemma is superior and R1 has a listed quirk: it can return empty responses on structured_output. • strategic_analysis — Gemma 5 vs R1 4; Gemma tied for 1st of 54. Gemma handles nuanced tradeoff reasoning with real numbers better in our tests.
• Ties (same score): creative_problem_solving (4), tool_calling (5), faithfulness (5), classification (4), long_context (5), persona_consistency (5), multilingual (5). Both models tied for 1st on many core capabilities like long_context and multilingual (ranked tied for 1st in the payload). Additional external benchmarks (Epoch AI) in the payload for R1 0528: MATH Level 5 = 96.6% and AIME 2025 = 66.4% (Epoch AI). Gemma has no external scores provided in the payload. Operational differences from the payload: Gemma supports text+image+video→text and a larger context window (262,144 vs R1's 163,840); R1 is text→text and exposes explicit reasoning tokens but also lists quirks (empty responses on certain structured/agentic tasks and a min_max_completion_tokens requirement).

Pricing (from the payload) — R1 0528: input $0.50/mTok, output $2.15/mTok. Gemma 4 26B A4B: input $0.08/mTok, output $0.35/mTok. That is a priceRatio of 6.142857 on output. Converting mTok → 1,000-token units (1M tokens = 1,000 mTok):

• 1M tokens (50% input / 50% output): R1 = $1,325 (500·$0.50 + 500·$2.15); Gemma = $215 (500·$0.08 + 500·$0.35). Difference: $1,110/month.
• 10M tokens (50/50): R1 = $13,250; Gemma = $2,150. Difference: $11,100/month.
• 100M tokens (50/50): R1 = $132,500; Gemma = $21,500. Difference: $111,000/month. Who should care: any high-volume deployer or startup — at scale the Gemma cost advantage becomes the dominant factor. Choose R1 only if its benchmark advantages (agentic_planning, safety, tool workflows) justify the large per-token premium.

Choose R1 0528 if you need: agentic planning, stronger safety calibration, better constrained rewriting, top-tier tool-calling and long-context behavior in our tests (R1 wins 3 of 5 head-to-head benchmarks and ranks tied for 1st across many categories). Choose Gemma 4 26B A4B if you need: reliable structured_output/JSON schema compliance, stronger strategic analysis, multimodal input (text+image+video), the larger 262,144 context window, or dramatically lower per-token cost (output $0.35 vs $2.15). If you run high-volume production (millions+ tokens/month), Gemma’s ~6.14× output cost advantage will usually dominate the decision unless R1’s specific wins materially improve product outcomes.