GPT-4.1 Nano vs Llama 3.3 70B Instruct

Across our 12-test internal benchmark suite, GPT-4.1 Nano wins 5 categories, Llama 3.3 70B Instruct wins 4, and 3 are tied. Neither model dominates — the split reflects genuinely different strengths.

Where GPT-4.1 Nano leads:

• Structured output (5 vs 4): GPT-4.1 Nano scores 5/5, tied for 1st among 54 models in our testing alongside 24 others. Llama scores 4/5 (rank 26 of 54). For JSON schema compliance, API integrations, and format-critical pipelines, GPT-4.1 Nano is the safer bet.
• Faithfulness (5 vs 4): GPT-4.1 Nano scores 5/5, tied for 1st among 55 models. Llama scores 4/5, ranked 34th. This matters for RAG applications and summarization where hallucinating details from source material is a failure mode.
• Constrained rewriting (4 vs 3): GPT-4.1 Nano ranks 6th of 53 on compression within hard character limits; Llama ranks 31st. Copy editing, SEO metadata, and length-constrained generation favor GPT-4.1 Nano.
• Persona consistency (4 vs 3): GPT-4.1 Nano ranks 38th of 53, Llama ranks 45th — both mid-field, but GPT-4.1 Nano holds the edge for chatbot and roleplay applications.
• Agentic planning (4 vs 3): GPT-4.1 Nano ranks 16th of 54; Llama ranks 42nd. Goal decomposition and failure recovery are meaningfully stronger in our testing, which matters for multi-step agentic workflows.

Where Llama 3.3 70B Instruct leads:

• Long context (5 vs 4): Llama scores 5/5, tied for 1st among 55 models. GPT-4.1 Nano scores 4/5 and ranks 38th. Counterintuitively, GPT-4.1 Nano has a far larger context window (1,047,576 tokens vs 131,072) — but Llama performs better on our 30K+ token retrieval test. Llama's window is more limited, but it uses it more effectively per our benchmarks.
• Classification (4 vs 3): Llama tied for 1st of 53 models at 4/5; GPT-4.1 Nano scores 3/5 at rank 31. Routing, intent detection, and categorization tasks go to Llama.
• Creative problem solving (3 vs 2): Llama ranks 30th of 54; GPT-4.1 Nano ranks 47th. Neither scores well in absolute terms, but Llama produces noticeably less generic ideas in our testing.
• Strategic analysis (3 vs 2): Llama ranks 36th of 54; GPT-4.1 Nano ranks 44th. Nuanced tradeoff reasoning favors Llama, though both sit below the 52-model median of 4.

Tied categories (both score equally):

• Tool calling (4/4): Both rank 18th of 54, sharing the score with 28 other models. Adequate for most function-calling use cases but not best-in-class.
• Safety calibration (2/2): Both rank 12th of 55, tied with 19 others. Below the p50 of 2 — in line with the field median but not a strength for either model.
• Multilingual (4/4): Both rank 36th of 55. Solid but not elite for non-English output.

External benchmarks (Epoch AI): Both models have scores on third-party math benchmarks. GPT-4.1 Nano scores 70% on MATH Level 5 (rank 11 of 14 models tested) and 28.9% on AIME 2025 (rank 20 of 23). Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 (rank 14 of 14) and 5.1% on AIME 2025 (rank 23 of 23). Neither model is competitive with the top math-focused models in the field on these external benchmarks, but GPT-4.1 Nano holds a substantial lead over Llama — 70% vs 41.6% on MATH Level 5 and 28.9% vs 5.1% on AIME 2025 per Epoch AI data. If mathematical reasoning is part of your workload, GPT-4.1 Nano is the clear choice between these two.

Both models share the same input cost at $0.10 per million tokens. The difference lives on the output side: GPT-4.1 Nano costs $0.40/M output tokens versus Llama 3.3 70B Instruct's $0.32/M — a 25% premium for GPT-4.1 Nano.

At real-world volumes:

• 1M output tokens/month: GPT-4.1 Nano costs $0.40 vs $0.32 — a difference of $0.08. Negligible.
• 10M output tokens/month: $4.00 vs $3.20 — you're saving $0.80/month with Llama 3.3 70B Instruct.
• 100M output tokens/month: $40.00 vs $32.00 — a real $8.00/month gap.

For most teams under 50M output tokens/month, this price gap is unlikely to be a deciding factor. At 100M+ tokens, cost-sensitive products (high-volume chatbots, large-scale document processing) will find Llama 3.3 70B Instruct's lower output rate meaningful. Developers who need image or file input will also note that Llama 3.3 70B Instruct is text-only per the payload, so GPT-4.1 Nano's multimodal support (text+image+file) may justify the premium regardless of volume.

Choose GPT-4.1 Nano if:

• Your pipeline depends on strict structured output (JSON, schemas, formatted responses) — it scores 5/5 and ties for 1st of 54 in our testing.
• You're building RAG systems or summarization tools where faithfulness to source material is critical — it scores 5/5 vs Llama's 4/5.
• You're deploying multi-step agentic workflows — GPT-4.1 Nano ranks 16th vs Llama's 42nd on agentic planning in our tests.
• You need image or file input alongside text — GPT-4.1 Nano supports multimodal input; Llama 3.3 70B Instruct is text-only per the payload.
• Math reasoning is part of your use case — GPT-4.1 Nano scores 70% vs 41.6% on MATH Level 5 (Epoch AI).
• You need a context window beyond 131K tokens — GPT-4.1 Nano supports over 1M tokens.

Choose Llama 3.3 70B Instruct if:

• Your task is primarily classification, routing, or intent detection — it ties for 1st of 53 models at 4/5 in our testing.
• You need strong long-context retrieval within a 131K window — it ties for 1st of 55 models at 5/5 on our long-context benchmark.
• Your use case involves strategic analysis or creative brainstorming — it outscores GPT-4.1 Nano on both (3 vs 2 each).
• You're running at high output volumes (100M+ tokens/month) and the $0.08/M output savings adds up — Llama costs $0.32/M vs $0.40/M.
• You want more sampling control — Llama's parameter support includes frequency_penalty, presence_penalty, min_p, top_k, logprobs, and top_logprobs, which GPT-4.1 Nano does not expose per the payload.