R1 0528 vs Gemini 2.5 Flash

Summary of our 12-test comparison (in our testing): R1 0528 wins 4 tests, Gemini 2.5 Flash wins 0, and 8 tests tie. Where R1 wins (scores A vs B): classification 4 vs 3 — meaning R1 is stronger at accurate routing/categorization in practice (ranked tied for 1st with 29 others out of 53). Faithfulness 5 vs 4 — R1 sticks to source material more reliably (R1 tied for 1st with 32 others out of 55). Strategic analysis 4 vs 3 — R1 does better on nuanced, numeric tradeoffs. Agentic planning 5 vs 4 — R1 better at goal decomposition and recovery (R1 tied for 1st with 14 others out of 54). Tests that tie (both models): long_context 5/5 — both excel at retrieval at 30K+ tokens (R1 tied for 1st, Gemini tied for 1st); tool_calling 5/5 — both choose and sequence functions accurately; creative_problem_solving 4/4, constrained_rewriting 4/4, structured_output 4/4, persona_consistency 5/5, multilingual 5/5, safety_calibration 4/4. Practical implications: R1’s edge in classification and faithfulness reduces hallucination and misrouting in production pipelines; its agentic planning and strategic-analysis wins matter for multi-step automation and numeric decision tasks. Additional data points: R1 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI) — useful if you care about third-party math benchmarking. Operational quirks: R1 returns empty responses on structured_output, constrained_rewriting, and agentic_planning in short tasks and uses reasoning tokens that consume output budget; plan for large min/max completion tokens. Feature differences from the payload: Gemini is multimodal (text+image+file+audio+video→text), supports a 1,048,576 token context and a 65,535 max output token limit — important for large-context, multimodal applications.

Prices from the payload: R1 0528 — input $0.50/mTok, output $2.15/mTok; Gemini 2.5 Flash — input $0.30/mTok, output $2.50/mTok. Assuming a 50/50 split of tokens between input and output (conservative for interactive apps), cost per 1M tokens = 500 mTok input + 500 mTok output: R1 = $0.50500 + $2.15500 = $1,325; Gemini = $0.30500 + $2.50500 = $1,400. At scale that gap is linear: 10M tokens → R1 $13,250 vs Gemini $14,000 (save $750); 100M → R1 $132,500 vs Gemini $140,000 (save $7,500). The key driver is R1’s lower output rate ($2.15 vs $2.50). Teams with heavy output generation (summaries, long responses, code dumps) should care most; the savings are $75 per 1M tokens in the 50/50 scenario and grow proportionally with volume.

Choose R1 0528 if you need the best classification, faithfulness and agentic planning from our 12-test suite and want a slightly lower ongoing output bill (R1 output $2.15/mTok vs Gemini $2.50/mTok). It’s the stronger pick for production routing, multi-step reasoning agents, and math-heavy workloads (MATH Level 5: 96.6% in Epoch AI’s test). Choose Gemini 2.5 Flash if you require multimodal inputs (images/audio/video/files), enormous context windows (1,048,576 tokens), or the largest single-response outputs (max_output_tokens 65,535) — those capabilities outweigh R1’s marginal benchmark edge for multimodal or extreme-context apps.