R1 0528 vs DeepSeek V3.1 Terminus

Across our 12-test suite, R1 0528 wins 7 tests, DeepSeek V3.1 Terminus wins 2, and 3 are ties. Test-by-test: - Tool calling: R1 0528 scores 5 vs Terminus 3 — R1 ties for 1st (tied with 16 others out of 54) on tool calling, so expect more accurate function selection and argument sequencing from R1. - Faithfulness: R1 5 vs Terminus 3 — R1 is tied for 1st (one of 33 top models of 55), indicating fewer hallucinations in our tests. - Persona consistency: R1 5 vs Terminus 4 — R1 is tied for 1st (36 others) so it better preserves character and resists injection. - Agentic planning: R1 5 vs Terminus 4 — R1 tied for 1st (14 others) and wins our goal decomposition/failure recovery scenarios. Note R1’s quirks: it “returns empty responses on structured_output” and uses reasoning tokens that consume output budget on short tasks — this can interfere with schema tasks despite high agentic/tool scores. - Classification: R1 4 vs Terminus 3 — R1 tied for 1st (29 others), meaning more reliable routing and categorization. - Safety calibration: R1 4 vs Terminus 1 — R1 ranks 6th of 55 (4 models share this score); Terminus ranks 32nd, so R1 is significantly better at refusing harmful requests while permitting legitimate ones. - Constrained rewriting: R1 4 vs Terminus 3 — R1 wins; better on tight character/constraint compression. - Structured output: Terminus 5 vs R1 4 — Terminus is tied for 1st (24 others) and wins JSON/schema tasks; R1’s documented empty_on_structured_output quirk explains why Terminus is superior for schema compliance. - Strategic analysis: Terminus 5 vs R1 4 — Terminus ties for 1st (25 others) on nuanced, numeric tradeoff reasoning where it edged R1. - Creative problem solving, long context, multilingual: ties (both score 4 or 5 depending), so expect comparable behavior on those tasks. - External math benchmarks (supplementary): R1 0528 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 — these Epoch AI results indicate strong math capability on the Level 5 benchmark. Overall, R1 is the stronger agentic and safety-calibrated model; Terminus wins when strict structured output and strategic-analysis scenarios dominate the workload.

Rates (per mTok): R1 0528 input $0.50 / output $2.15; DeepSeek V3.1 Terminus input $0.21 / output $0.79. If you assume a 50/50 split of input/output tokens, monthly costs are: 1M tokens — R1: $1,325; Terminus: $500 (R1 is +$825). 10M tokens — R1: $13,250; Terminus: $5,000 (R1 +$8,250). 100M tokens — R1: $132,500; Terminus: $50,000 (R1 +$82,500). If you bill only output tokens, 1M output tokens cost $2,150 (R1) vs $790 (Terminus). The ~2.72× price ratio means cost-conscious deployments and high-volume apps (10M+ tokens/month) should strongly prefer V3.1 Terminus; teams that need R1’s higher tool-calling fidelity and faithfulness can justify the higher spend at lower volumes or mission-critical use cases.

Choose R1 0528 if you build agentic systems, tool-enabled assistants, or applications where faithfulness, persona consistency, tool calling, and safety calibration matter — you’ll pay ~2.72× more but gain higher tool and safety performance. Choose DeepSeek V3.1 Terminus if you need strict JSON/schema compliance or lower operating cost at scale (it wins structured output and strategic analysis and costs $0.79 vs $2.15 per mTok output). If you’re volume-sensitive (10M+ tokens/month) or your product relies on reliable structured output, pick Terminus; if correctness of tool invocation and refusal behavior is the priority, pick R1 0528.