R1 vs Llama 3.3 70B Instruct

Across our 12-test suite, R1 wins 7 benchmarks, Llama 3.3 70B Instruct wins 3, and they tie on 2.

Where R1 dominates:

• Strategic analysis: R1 scores 5/5 (tied for 1st with 25 others out of 54 tested); Llama 3.3 70B Instruct scores 3/5 (rank 36 of 54). This is a meaningful gap — R1's reasoning depth shows up clearly in nuanced tradeoff tasks with real numbers.
• Creative problem solving: R1 scores 5/5 (tied for 1st with 7 others out of 54); Llama 3.3 70B Instruct scores 3/5 (rank 30 of 54). If you need non-obvious, feasible ideas rather than generic suggestions, R1 has a clear edge.
• Faithfulness: R1 scores 5/5 (tied for 1st with 32 others out of 55); Llama 3.3 70B Instruct scores 4/5 (rank 34 of 55). R1 sticks closer to source material — important for summarization and RAG pipelines.
• Persona consistency: R1 scores 5/5 (tied for 1st with 36 others out of 53); Llama 3.3 70B Instruct scores 3/5 (rank 45 of 53). R1 maintains character and resists prompt injection significantly better.
• Agentic planning: R1 scores 4/5 (rank 16 of 54); Llama 3.3 70B Instruct scores 3/5 (rank 42 of 54). For goal decomposition and multi-step workflows, R1 is more reliable.
• Multilingual: R1 scores 5/5 (tied for 1st with 34 others out of 55); Llama 3.3 70B Instruct scores 4/5 (rank 36 of 55). Both are capable, but R1 reaches the ceiling.
• Constrained rewriting: R1 scores 4/5 (rank 6 of 53); Llama 3.3 70B Instruct scores 3/5 (rank 31 of 53). Compressing content within hard character limits is a clear R1 strength.

Where Llama 3.3 70B Instruct wins:

• Classification: Llama 3.3 70B Instruct scores 4/5 (tied for 1st with 29 others out of 53); R1 scores 2/5 (rank 51 of 53). This is R1's weakest result — near the bottom of all tested models. For routing and categorization tasks, Llama 3.3 70B Instruct is the clear choice.
• Long context: Llama 3.3 70B Instruct scores 5/5 (tied for 1st with 36 others out of 55); R1 scores 4/5 (rank 38 of 55). Llama also has a 131K context window vs R1's 64K, giving it a structural advantage on document-heavy tasks.
• Safety calibration: Llama 3.3 70B Instruct scores 2/5 (rank 12 of 55); R1 scores 1/5 (rank 32 of 55). Neither model scores well here — safety calibration is a weak spot across the board — but Llama 3.3 70B Instruct is measurably better.

Ties:

• Structured output and tool calling are tied at 4/5 each, both ranking 18th of 54 on tool calling and 26th of 54 on structured output. Neither model has an edge for function-calling or JSON-schema workflows.

External benchmarks (Epoch AI): On MATH Level 5, R1 scores 93.1% (rank 8 of 14) versus Llama 3.3 70B Instruct's 41.6% (rank 14 of 14, last of all tested models). On AIME 2025, R1 scores 53.3% (rank 17 of 23) versus Llama 3.3 70B Instruct's 5.1% (rank 23 of 23, last). These external benchmarks confirm that R1 has substantially stronger mathematical reasoning than Llama 3.3 70B Instruct — a gap that our internal scores on strategic analysis and creative problem solving also reflect.

R1 costs $0.70/M input tokens and $2.50/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — making it 7x cheaper on input and 7.8x cheaper on output. At 1M output tokens/month, that's $2.50 vs $0.32 — a difference of $2.18. At 10M output tokens, it's $25 vs $3.20 — a $21.80 gap. At 100M output tokens, R1 costs $250 vs Llama 3.3 70B Instruct's $32 — you're paying $218 more per month for the performance uplift. For developers running lightweight classification pipelines, customer-facing chatbots with high traffic, or any workload where output volume is high and complex reasoning isn't required, Llama 3.3 70B Instruct is meaningfully cheaper. For lower-volume tasks where analytical depth drives business value — contract analysis, strategic planning, research synthesis — R1's $2.50/M output rate is easier to justify.

Choose R1 if:

• Your tasks require deep reasoning, multi-step analysis, or creative problem solving — it scores 5/5 on strategic analysis, creative problem solving, and faithfulness in our testing.
• You're building agentic or multi-step pipelines where goal decomposition matters (4/5 vs Llama's 3/5).
• Mathematical reasoning is part of your workflow — R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI) vs Llama's 41.6% and 5.1%.
• You need reliable persona consistency for chatbot or roleplay applications (5/5 vs 3/5).
• Your output volume is moderate enough that the $2.50/M output cost is manageable (roughly under 10M tokens/month if budget is tight).

Choose Llama 3.3 70B Instruct if:

• Classification and routing are your primary use case — it scores 4/5 (tied for 1st among 53 models) while R1 scores a poor 2/5 (rank 51 of 53).
• You need a 131K context window — R1 caps at 64K.
• You're running high-volume workloads where the 7.8x output cost difference ($2.50 vs $0.32/M tokens) adds up to hundreds of dollars per month.
• Safety calibration matters to your deployment — Llama 3.3 70B Instruct scores higher on our safety test (2/5 vs R1's 1/5), though neither is strong.
• You want a simpler API integration without reasoning token quirks — R1 has specific requirements including a 1,000-token minimum on max completion tokens.