GPT-5 Nano vs Llama 3.3 70B Instruct

Across our 12 internal benchmark tests, GPT-5 Nano wins 6, Llama 3.3 70B Instruct wins 1, and the two tie on 5.

Where GPT-5 Nano leads:

• Structured Output (5 vs 4): GPT-5 Nano ties for 1st among 54 models tested; Llama 3.3 70B Instruct ranks 26th. For applications requiring reliable JSON schema compliance — APIs, form parsing, data pipelines — this is a meaningful gap.
• Strategic Analysis (4 vs 3): GPT-5 Nano ranks 27th of 54 with 9 models sharing that score; Llama 3.3 70B Instruct ranks 36th. This covers nuanced tradeoff reasoning with real numbers — relevant for business analysis, financial modeling prompts, and decision-support tools.
• Safety Calibration (4 vs 2): GPT-5 Nano ranks 6th of 55, sharing the score with only 3 others. Llama 3.3 70B Instruct scores 2, ranking 12th but sharing that score with 19 others — well below the median. This is a standout gap: GPT-5 Nano is far more calibrated at refusing harmful requests while still permitting legitimate ones. For any deployment with public-facing users, this matters.
• Persona Consistency (4 vs 3): GPT-5 Nano ranks 38th of 53 — not high in absolute terms, but Llama 3.3 70B Instruct ranks 45th. For chatbot and assistant use cases requiring stable character maintenance and injection resistance, GPT-5 Nano is the safer choice.
• Agentic Planning (4 vs 3): GPT-5 Nano ranks 16th of 54; Llama 3.3 70B Instruct ranks 42nd. Goal decomposition and failure recovery are core requirements for agentic workflows, making this one of the most practically significant gaps in this comparison. Tool calling enables agentic workflows, and reliable planning is the backbone of multi-step execution.
• Multilingual (5 vs 4): GPT-5 Nano ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 36th. Equivalent non-English output quality is critical for international products — GPT-5 Nano is at the ceiling here.

Where Llama 3.3 70B Instruct leads:

• Classification (4 vs 3): Llama 3.3 70B Instruct ties for 1st among 53 models — a strong result. GPT-5 Nano ranks 31st. For routing, tagging, and categorization pipelines where classification is the primary task, Llama 3.3 70B Instruct has a genuine edge.

Ties (both models score identically):

• Constrained Rewriting: both 3/5 (rank 31 of 53)
• Creative Problem Solving: both 3/5 (rank 30 of 54)
• Tool Calling: both 4/5 (rank 18 of 54)
• Faithfulness: both 4/5 (rank 34 of 55)
• Long Context: both 5/5 (tied for 1st among 55 models)

External Benchmarks (Epoch AI):

The math performance gap is extreme. On MATH Level 5 (competition math), GPT-5 Nano scores 95.2% — ranking 7th of 14 models with this data — while Llama 3.3 70B Instruct scores 41.6%, ranking last (14th of 14). On AIME 2025 (math olympiad), GPT-5 Nano scores 81.1% (14th of 23 models), while Llama 3.3 70B Instruct scores 5.1% — last of all 23 models tested by Epoch AI. These are not marginal differences; Llama 3.3 70B Instruct is near the floor on both external math benchmarks. For any application with quantitative reasoning, scientific computation, or math tutoring components, this is disqualifying.

GPT-5 Nano charges $0.05/M input tokens and $0.40/M output tokens. Llama 3.3 70B Instruct charges $0.10/M input and $0.32/M output. The direction of the gap depends on your token mix. At output-heavy workloads (e.g., long-form generation), GPT-5 Nano is more expensive: at 10M output tokens/month, GPT-5 Nano costs $4.00 vs Llama 3.3 70B Instruct's $3.20 — an $0.80 difference. At input-heavy workloads (e.g., document processing, RAG with large context windows), GPT-5 Nano is cheaper: at 10M input tokens/month, GPT-5 Nano costs $0.50 vs Llama 3.3 70B Instruct's $1.00. At 100M tokens/month in a mixed input/output scenario, the difference will land somewhere between -$5 and +$8 depending on your ratio — not a business-level budget decision for most teams. The more meaningful consideration is that GPT-5 Nano's 400K context window dwarfs Llama 3.3 70B Instruct's 131K, which could eliminate the need for chunking strategies and their associated engineering costs.

Choose GPT-5 Nano if your application involves agentic workflows (it ranks 16th vs Llama's 42nd on agentic planning), structured data extraction (tied for 1st on structured output vs Llama's 26th), multilingual users (tied for 1st vs Llama's 36th), any math or quantitative reasoning (95.2% vs 41.6% on MATH Level 5 per Epoch AI), or public-facing products where safety calibration is non-negotiable (ranks 6th vs Llama's 12th with a lower score). GPT-5 Nano's 400K context window is also the decisive factor if your use case involves long documents, large codebases, or retrieval over extended conversation history.

Choose Llama 3.3 70B Instruct if your primary task is classification and routing — it ties for 1st among 53 models on that benchmark, outperforming GPT-5 Nano's 31st-place score of 3/5. It also generates output tokens slightly more cheaply ($0.32/M vs $0.40/M), which matters at very high output-heavy volumes. If you're building a high-throughput categorization or tagging pipeline where math, planning, and safety are not concerns, Llama 3.3 70B Instruct is a cost-effective fit.