R1 0528 vs Gemini 2.5 Pro

Head-to-head by test (our 12-test suite):

• Ties: strategic_analysis (4 vs 4), tool_calling (5 vs 5), faithfulness (5 vs 5), classification (4 vs 4), long_context (5 vs 5), persona_consistency (5 vs 5), multilingual (5 vs 5). Those ties mean both models are functionally equivalent on nuanced reasoning, tool selection, hallucination avoidance, categorization, very-long context retrieval, character consistency, and multilingual outputs in our tests.
• R1 0528 wins: constrained_rewriting 4 vs 3 (R1 rank 6 of 53 vs Gemini rank 31 of 53) — better at squeezing content into hard limits; safety_calibration 4 vs 1 (R1 rank 6 of 55 vs Gemini rank 32) — R1 refuses harmful requests more reliably in our testing; agentic_planning 5 vs 4 (R1 tied for 1st vs Gemini rank 16) — R1 better at goal decomposition and recovery.
• Gemini 2.5 Pro wins: structured_output 5 vs 4 (Gemini tied for 1st vs R1 rank 26) — stronger JSON/schema adherence; creative_problem_solving 5 vs 4 (Gemini tied for 1st vs R1 rank 9) — more non-obvious feasible ideas in our tests. External benchmarks (Epoch AI): on MATH Level 5 (Epoch AI) R1 scores 96.6% (rank 5 of 14), while Gemini lacks a MATH Level 5 entry in our payload; on AIME 2025 (Epoch AI) Gemini scores 84.2% (rank 11 of 23) vs R1 66.4% (rank 16 of 23), indicating Gemini’s advantage on AIME-style olympiad problems in these external measures. On SWE-bench Verified (Epoch AI) Gemini scores 57.6% (rank 10 of 12); R1 has no SWE-bench Verified value in the payload, so we cannot credit R1 on that external coding benchmark. Rankings context: both models are tied for 1st on faithfulness, long_context, tool_calling, persona_consistency, and multilingual in our set, so practical differences emerge in the few tests they diverge on (format fidelity, creativity, safety, constrained rewriting, agentic planning). Note R1’s listed quirk: it can return empty responses on structured_output and constrained_rewriting unless given high max completion tokens and it uses reasoning tokens that consume output budget — an operational caveat despite its strong scores.

Per-1M token math (cost per mTok * 1,000): R1 0528 = $0.50 input → $500 / 1M, $2.15 output → $2,150 / 1M; combined 1M in+1M out = $2,650. Gemini 2.5 Pro = $1.25 input → $1,250 / 1M, $10.00 output → $10,000 / 1M; combined 1M in+1M out = $11,250. At scale: for 10M in+10M out multiply by 10 (R1 = $26,500; Gemini = $112,500). For 100M in+100M out (R1 = $265,000; Gemini = $1,125,000). The gap matters most for high-output apps (chatbot transcripts, generation-heavy APIs) where Gemini’s output price ($10 / 1k) drives large bills. Low-volume, high-fidelity multimodal research may accept Gemini’s premium; production services with millions of tokens/month should prefer R1 to cut costs by roughly $8,600 per 1M in+out tokens in the example above.

Choose R1 0528 if you need a cost-effective, safe, agentic LLM for high-volume production: it wins more of our benchmarks (3 vs 2), scores 5/5 on agentic_planning, faithfulness, long_context, and persona_consistency, and costs $0.50 in / $2.15 out per 1k. Choose Gemini 2.5 Pro if you must have best-in-class structured output and creative problem solving, multimodal input support (text+image+file+audio+video→text), or stronger AIME performance (84.2% on AIME 2025, Epoch AI) and you can absorb much higher bills ($1.25 in / $10.00 out per 1k).