R1 0528 vs Llama 3.3 70B Instruct

R1 0528 wins 9 of 12 benchmarks in our testing, ties 3, and loses none. Here's the test-by-test breakdown:

Where R1 0528 leads decisively:

• Persona consistency: 5 vs 3. R1 0528 ties for 1st among 53 models; Llama 3.3 70B Instruct ranks 45th of 53. This gap matters for chatbot and roleplay applications where character coherence under adversarial prompting is critical.
• Agentic planning: 5 vs 3. R1 0528 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 42nd of 54. Goal decomposition and failure recovery are core to multi-step AI agents — this is a meaningful capability gap.
• Tool calling: 5 vs 4. R1 0528 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 18th. Function selection, argument accuracy, and call sequencing are notably stronger in R1 0528.
• Faithfulness: 5 vs 4. R1 0528 ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 34th. For RAG pipelines and document summarization, R1 0528 is less likely to hallucinate beyond source material.
• Multilingual: 5 vs 4. R1 0528 ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 36th. Non-English output quality is meaningfully better.
• Safety calibration: 4 vs 2. R1 0528 ranks 6th of 55; Llama 3.3 70B Instruct ranks 12th with a score of 2 — below the 50th percentile (p50 = 2). R1 0528 is significantly better calibrated at refusing harmful requests while permitting legitimate ones.
• Creative problem solving: 4 vs 3. R1 0528 ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th.
• Constrained rewriting: 4 vs 3. R1 0528 ranks 6th of 53; Llama 3.3 70B Instruct ranks 31st.
• Strategic analysis: 4 vs 3. R1 0528 ranks 27th of 54; Llama 3.3 70B Instruct ranks 36th. Both are mid-field here — neither excels at nuanced tradeoff reasoning relative to the full model pool.

Where they tie:

• Classification: Both score 4, both tied for 1st among 53 models (30 models share this score). No practical difference.
• Structured output: Both score 4, both rank 26th of 54 (27 models share this score). JSON schema compliance is equivalent.
• Long context: Both score 5, both tied for 1st among 55 models. Retrieval accuracy at 30K+ tokens is identical.

External benchmarks (Epoch AI): The math gap is extreme. On MATH Level 5, R1 0528 scores 96.6% (rank 5 of 14 models tested) vs Llama 3.3 70B Instruct's 41.6% (rank 14 of 14 — last place). On AIME 2025, R1 0528 scores 66.4% (rank 16 of 23) vs Llama 3.3 70B Instruct's 5.1% (rank 23 of 23 — last place). Competition-level math is simply not a use case for Llama 3.3 70B Instruct.

Important R1 0528 quirk to note: The payload flags that R1 0528 returns empty responses on structured output, constrained rewriting, and agentic planning tasks when max completion tokens is set too low — reasoning tokens consume the output budget. You must set a high max_completion_tokens value (minimum 1000 floor enforced) when using this model in production.

R1 0528 is priced at $0.50/M input and $2.15/M output tokens. Llama 3.3 70B Instruct comes in at $0.10/M input and $0.32/M output — making it 5x cheaper on input and 6.7x cheaper on output. At real-world volumes, the gap compounds fast. At 1M output tokens/month, R1 0528 costs $2,150 vs $320 for Llama 3.3 70B Instruct — a $1,830 difference. At 10M output tokens, that's $21,500 vs $3,200 ($18,300 gap). At 100M output tokens, you're looking at $215,000 vs $32,000 — a $183,000 annual delta. For developers running high-volume inference on tasks where both models score identically (classification, structured output, long context), the cost argument for Llama 3.3 70B Instruct is strong. The premium for R1 0528 is justified when you need its reasoning depth — agentic workflows, tool orchestration, math, or multilingual quality — but budget-conscious teams should model their actual task mix before defaulting to the pricier option.

Choose R1 0528 if:

• You're building agentic systems that require multi-step planning, tool orchestration, or failure recovery — it scores 5/5 on both agentic planning and tool calling in our tests vs 3/5 and 4/5 for Llama 3.3 70B Instruct.
• Your application needs accurate reasoning over mathematics or complex logic — R1 0528 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI) vs 41.6% and 5.1% respectively.
• You need strong multilingual output quality (5 vs 4), high faithfulness in RAG pipelines (5 vs 4), or reliable persona consistency in chatbot applications (5 vs 3).
• Safety calibration matters — R1 0528 scores 4 vs Llama 3.3 70B Instruct's 2, which falls below the median for models in our dataset.
• Output volume is moderate enough that the $2.15/M token cost is acceptable for the quality gains.

Choose Llama 3.3 70B Instruct if:

• Your workload is dominated by classification, structured output, or long-context retrieval — all benchmarks where both models tie.
• You're running at high output volumes (10M+ tokens/month) and the $1.83/M output token savings materially affects your budget.
• Your tasks don't require deep reasoning, complex tool chains, or math — the simpler architecture is faster and dramatically cheaper.
• You need logprobs or top_logprobs support — these parameters are available in Llama 3.3 70B Instruct but not listed for R1 0528 in the payload.
• You want a predictable, quirk-free inference experience without managing reasoning token budget constraints.