GPT-5.4 Nano vs Llama 3.3 70B Instruct

Across our 12-test internal benchmark suite, GPT-5.4 Nano wins 8 categories, ties 3, and loses 1 to Llama 3.3 70B Instruct.

Where GPT-5.4 Nano leads:

• Structured output (5 vs 4): GPT-5.4 Nano ties for 1st among 54 models; Llama ranks 26th. For JSON schema compliance and API integrations that must output reliable structured data, this is a meaningful edge.
• Strategic analysis (5 vs 3): GPT-5.4 Nano ties for 1st among 54 models; Llama ranks 36th. That two-point gap means noticeably better nuanced tradeoff reasoning — relevant for financial, product, or operational analysis tasks.
• Persona consistency (5 vs 3): GPT-5.4 Nano ties for 1st among 53 models; Llama ranks 45th. This matters for chatbot, roleplay, or branded assistant applications where character drift is a real problem.
• Multilingual (5 vs 4): GPT-5.4 Nano ties for 1st among 55 models; Llama ranks 36th. A full point advantage for non-English use cases — significant if you're serving global users.
• Agentic planning (4 vs 3): GPT-5.4 Nano ranks 16th of 54; Llama ranks 42nd. Better goal decomposition and failure recovery translates directly to more reliable autonomous agent pipelines.
• Constrained rewriting (4 vs 3): GPT-5.4 Nano ranks 6th of 53; Llama ranks 31st. For compression tasks with hard character limits — ad copy, UI text — this difference shows up in practice.
• Creative problem solving (4 vs 3): GPT-5.4 Nano ranks 9th of 54; Llama ranks 30th.
• Safety calibration (3 vs 2): GPT-5.4 Nano ranks 10th of 55 with 2 models sharing that score; Llama ranks 12th with 20 models at the same level. Both land above the field's p75 of 2, but GPT-5.4 Nano's score reflects more precise calibration between harmful refusals and legitimate requests.

Where they tie:

• Tool calling (4/4): Both rank 18th of 54, sharing the score with 29 models. Adequate for most function-calling use cases, but neither model leads here.
• Faithfulness (4/4): Both rank 34th of 55 — identical performance on sticking to source material.
• Long context (5/5): Both tie for 1st among 55 models. GPT-5.4 Nano's 400K context window dwarfs Llama's 131K, which could matter operationally even if both score identically on our 30K+ retrieval test.

Where Llama 3.3 70B Instruct wins:

• Classification (4 vs 3): Llama ties for 1st among 53 models; GPT-5.4 Nano ranks 31st with 20 models sharing that score. For routing, categorization, and tagging pipelines, Llama is the stronger choice.

External benchmarks (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025, ranking 8th of 23 models tested — well above the field median of 83.9%. Llama 3.3 70B Instruct scores 5.1% on AIME 2025 (rank 23 of 23) and 41.6% on MATH Level 5 (rank 14 of 14, last among models tested). These are third-party scores from Epoch AI, not our internal testing, but they paint a stark picture: GPT-5.4 Nano is competitive at olympiad-level math; Llama 3.3 70B Instruct struggles significantly on advanced math reasoning by these external measures.

GPT-5.4 Nano costs $0.20/M input tokens and $1.25/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — half the input cost and less than a quarter of the output cost. At 1M output tokens/month, the gap is $930. At 10M tokens, you're paying $9,300 more for GPT-5.4 Nano. At 100M tokens, the difference reaches $93,000 per month in output costs alone. Developers running batch pipelines, content generation at scale, or cost-sensitive APIs will feel that gap immediately. Llama 3.3 70B Instruct's pricing makes it one of the more affordable options on the market — its $0.32/M output cost sits well below the $1.25 median of premium models. GPT-5.4 Nano's pricing is competitive for a capable closed model, and its benchmark advantage may justify the premium for applications where quality on reasoning, structured output, or multilingual tasks is business-critical.

Choose GPT-5.4 Nano if:

• Your application requires structured output reliability — it outranks Llama significantly in our JSON compliance tests.
• You're building multilingual products serving non-English speakers.
• Agentic workflows or multi-step planning are core to your system — GPT-5.4 Nano scores 4 vs Llama's 3 and ranks 26 spots higher.
• Persona consistency matters (chatbots, branded assistants, roleplay) — GPT-5.4 Nano scores 5 vs Llama's 3, ranking 1st vs 45th.
• Math or reasoning tasks appear in your pipeline — its 87.8% AIME 2025 score (Epoch AI) vastly outperforms Llama's 5.1%.
• You need a 400K context window rather than Llama's 131K.
• You can accept paying $1.25/M output tokens for a measurable quality boost.

Choose Llama 3.3 70B Instruct if:

• Classification and routing are your primary use case — it ties for 1st of 53 models in our testing while GPT-5.4 Nano ranks 31st.
• Cost is a hard constraint: at $0.32/M output tokens, Llama is 75% cheaper on outputs — saving $93,000/month at 100M tokens compared to GPT-5.4 Nano.
• You're running high-volume batch inference where quality differences in reasoning or persona don't affect outcomes.
• You want broader sampling control — Llama supports temperature, top_p, top_k, min_p, logprobs, and logit_bias, parameters not present in GPT-5.4 Nano's supported parameter list.
• You need an open-ecosystem model with wide hosting options across providers.