R1 0528 vs Mistral Large 3 2512

Overview: Across our 12-test suite R1 0528 wins 8 tests, Mistral Large 3 2512 wins 1, and 3 are ties (strategic_analysis, faithfulness, multilingual). Detailed walk-through:

• Tool calling: R1 0528 scores 5 vs Mistral 4. R1 is tied for 1st on our tool_calling ranking ("tied for 1st with 16 other models out of 54 tested"), so in practice R1 is more reliable at selecting functions, arguments and sequencing.

• Long context: R1 0528 scores 5 vs Mistral 4. R1 ranks tied for 1st on long_context ("tied for 1st with 36 other models out of 55 tested"), meaning R1 preserves retrieval accuracy at 30K+ tokens better in our tests. Mistral’s long_context rank is lower (rank 38 of 55), so you should expect more dropoff on very long documents.

• Persona consistency: R1 5 vs Mistral 3. R1 is tied for 1st in persona_consistency ("tied for 1st with 36 other models out of 53 tested"), so it resists prompt injection and keeps character/role consistency better.

• Faithfulness: tie at 5 each. Both models score top marks for sticking to source material; both are tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55 tested").

• Safety calibration: R1 4 vs Mistral 1. R1 ranks 6 of 55 on safety_calibration ("rank 6 of 55"), while Mistral sits much lower (rank 32 of 55). In our tests R1 refused harmful prompts and allowed legitimate ones more reliably.

• Classification: R1 4 vs Mistral 3. R1 is tied for 1st on classification ("tied for 1st with 29 other models out of 53 tested"), so routing/categorization tasks favor R1.

• Structured output (JSON/schema): Mistral 5 vs R1 4. Mistral is tied for 1st on structured_output ("tied for 1st with 24 other models out of 54 tested"); R1 scores lower and also has a documented quirk: it "returns empty responses on structured_output" in the payload, which explains Mistral’s advantage for strict schema compliance. Use Mistral when format adherence is non-negotiable.

• Creative problem solving & constrained rewriting: R1 wins both (creative 4 vs 3; constrained 4 vs 3). R1 ranks higher (creative: rank 9 of 54; constrained_rewriting: rank 6 of 53), indicating better generation of specific feasible ideas and compression within hard limits.

• Agentic planning: R1 5 vs Mistral 4. R1 is tied for 1st on agentic_planning, so goal decomposition and recovery behavior were stronger in our tests.

• Strategic analysis & Multilingual: ties at 4 and 5 respectively. Both models performed comparably on nuanced tradeoff reasoning and non-English output in our suite.

Supplementary external math benchmarks (Epoch AI): R1 0528 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI). Mistral Large 3 2512 has no external math scores in the payload. Note: our 1–5 internal scores and the external percentage metrics are different systems and are shown for complementary context only.

Practical meaning: R1 is the safer, more capable option for tool-driven, long-context and safety-sensitive applications; Mistral is the clear choice if you need strict JSON/schema outputs and lower output cost.

Per the payload, R1 0528 charges $0.50 per M input tokens and $2.15 per M output tokens; Mistral Large 3 2512 charges $0.50 per M input and $1.50 per M output. Combined per-million-token cost: R1 = $2.65/M, Mistral = $2.00/M (price ratio ≈ 1.433). At 1M tokens/month the delta is $0.65; at 10M it's $6.50; at 100M it's $65.00. Teams doing low-volume experiments won't feel the difference, but high-volume production (10M–100M+ tokens/month) should budget the extra $6.50–$65/month for R1 if its accuracy on tool calling, long contexts and safety matters. Cost-sensitive services that must obey strict JSON schemas should prefer Mistral to shave ~24% off per-token spend.

Choose R1 0528 if: you need reliable tool calling, long-context retrieval at 30K+ tokens, stronger safety calibration, or better persona consistency and agentic planning — and you can absorb ~43% higher output costs (R1 output $2.15/M vs Mistral $1.50/M).

Choose Mistral Large 3 2512 if: your primary requirement is strict structured output (JSON/schema compliance) and lower per-output-token cost, or you run very high-volume workloads where every $0.65/M saved compounds into meaningful monthly savings.