GPT-5 vs Llama 3.3 70B Instruct

Our 12-test internal benchmark suite (scored 1–5) shows GPT-5 winning 9 tests outright, with 3 ties and zero losses to Llama 3.3 70B Instruct.

Where GPT-5 dominates:

• Agentic planning: 5 vs 3. GPT-5 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 42nd of 54. For multi-step AI workflows with goal decomposition and error recovery, this gap is operationally significant.
• Strategic analysis: 5 vs 3. GPT-5 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 36th. Tasks requiring nuanced tradeoff reasoning with real data will surface this difference quickly.
• Persona consistency: 5 vs 3. GPT-5 ties for 1st among 53 models; Llama 3.3 70B Instruct ranks 45th of 53—near the bottom. For chatbots or roleplay applications that must maintain character under adversarial prompting, Llama 3.3 70B Instruct is a meaningful liability here.
• Faithfulness: 5 vs 4. GPT-5 ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 34th. In RAG pipelines where hallucination costs are high, GPT-5's edge matters.
• Tool calling: 5 vs 4. GPT-5 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 18th. Function selection accuracy and argument handling are better on GPT-5—relevant for any agentic or API-calling application.
• Multilingual: 5 vs 4. GPT-5 ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 36th. Non-English output quality gap is real.
• Structured output: 5 vs 4. GPT-5 ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 26th.
• Creative problem solving: 4 vs 3. GPT-5 ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th.
• Constrained rewriting: 4 vs 3. GPT-5 ranks 6th of 53; Llama 3.3 70B Instruct ranks 31st.

Where they tie:

• Classification: Both score 4/5, both tied for 1st among 53 models. For routing and categorization tasks, Llama 3.3 70B Instruct is a direct peer.
• Long context: Both score 5/5, both tied for 1st among 55 models. Retrieval accuracy at 30K+ tokens is equivalent—though note GPT-5's context window is 400K tokens vs Llama 3.3 70B Instruct's 128K.
• Safety calibration: Both score 2/5, both rank 12th of 55. Neither model distinguishes itself here; both sit below the field median.

External benchmarks (Epoch AI): On math, GPT-5 scores 98.1% on MATH Level 5 (rank 1 of 14 models, sole holder) and 91.4% on AIME 2025 (rank 6 of 23). Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 (rank 14 of 14, last place) and 5.1% on AIME 2025 (rank 23 of 23, last place). The math gap is not marginal—it's categorical. On SWE-bench Verified, GPT-5 scores 73.6% (rank 6 of 12), placing it in the upper half of models tested on real GitHub issue resolution. Llama 3.3 70B Instruct has no SWE-bench score in the payload.

GPT-5 costs $1.25/M input tokens and $10/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output—making it 12.5x cheaper on input and 31x cheaper on output.

At 1M output tokens/month: GPT-5 costs $10, Llama 3.3 70B Instruct costs $0.32. Negligible in absolute terms, but the ratio matters at scale.

At 10M output tokens/month: GPT-5 runs $100, Llama 3.3 70B Instruct runs $3.20. The $96.80 monthly delta starts to sting for bootstrapped products.

At 100M output tokens/month: GPT-5 costs $1,000, Llama 3.3 70B Instruct costs $32. That $968 monthly gap is a meaningful infrastructure budget line for any team.

Who should care: API developers building products with unpredictable or high output volume—chatbots, document processors, summarization pipelines—will feel the cost gap acutely. Teams running GPT-5 at scale need a clear, measurable quality requirement to justify the premium. Consumer-facing apps where output quality is subjectively good enough with Llama 3.3 70B Instruct should default to the cheaper option.

Choose GPT-5 if:

• Your application involves agentic workflows, multi-step tool use, or autonomous planning—it scores 5 vs 3 on agentic planning in our tests.
• You need reliable math or reasoning: 98.1% on MATH Level 5 and 91.4% on AIME 2025 (Epoch AI) are in a different league than Llama 3.3 70B Instruct's 41.6% and 5.1%.
• Persona consistency is critical (chatbots, branded assistants)—GPT-5 scores 5 vs 3 and Llama 3.3 70B Instruct ranks 45th of 53 models on this dimension.
• You need a 400K token context window (vs Llama 3.3 70B Instruct's 128K).
• You need multimodal input: GPT-5 accepts text, images, and files; Llama 3.3 70B Instruct is text-only per the payload.
• Quality is the hard constraint and volume is manageable.

Choose Llama 3.3 70B Instruct if:

• Your primary use case is classification or long-context retrieval—it ties GPT-5 on both at a fraction of the cost.
• You're running high-volume workloads where the 31x output cost difference ($10 vs $0.32/M tokens) is material to your unit economics.
• Your tasks don't require advanced reasoning, agentic behavior, or complex math.
• You want text-only generation with extensive sampling parameter control (logprobs, top_k, min_p, repetition_penalty—parameters GPT-5 does not support per the payload).
• Budget is a constraint and good-enough quality at 4/5 on classification and long context satisfies your requirements.