GPT-4o-mini vs Llama 3.3 70B Instruct

Across our 12-test internal suite, Llama 3.3 70B Instruct wins 4 benchmarks, GPT-4o-mini wins 2, and 6 are tied.

Where Llama 3.3 70B Instruct wins:

• Long context (5 vs 4): Llama 3.3 70B Instruct is tied for 1st among 55 models in our long-context retrieval test (accuracy at 30K+ tokens), while GPT-4o-mini ranks 38th of 55. For RAG pipelines and document-heavy workloads, this is a meaningful gap.
• Faithfulness (4 vs 3): Llama 3.3 70B Instruct ranks 34th of 55 on sticking to source material without hallucinating; GPT-4o-mini ranks 52nd of 55 — near the bottom. If your use case involves summarization or grounded Q&A, this difference is operationally important.
• Strategic analysis (3 vs 2): Both models score below the field median (p50 = 4), but Llama 3.3 70B Instruct ranks 36th vs GPT-4o-mini's 44th of 54. Neither excels at nuanced tradeoff reasoning with real numbers.
• Creative problem solving (3 vs 2): Llama 3.3 70B Instruct ranks 30th of 54 on generating non-obvious, feasible ideas; GPT-4o-mini ranks 47th. For ideation tasks, 70B Instruct has a clear edge.

Where GPT-4o-mini wins:

• Safety calibration (4 vs 2): GPT-4o-mini ranks 6th of 55 in our testing, refusing harmful requests while permitting legitimate ones. Llama 3.3 70B Instruct ranks 12th but scores only 2/5 — well below the field median (p50 = 2, p75 = 2, so the bar is low, but GPT-4o-mini clears it far more reliably). For regulated industries or consumer-facing deployments, this is a significant differentiator.
• Persona consistency (4 vs 3): GPT-4o-mini ranks 38th of 53; Llama 3.3 70B Instruct ranks 45th. GPT-4o-mini maintains character and resists injection attacks more reliably — relevant for roleplay apps and branded assistants.

Tied benchmarks (6 of 12): Both models score identically on structured output (4/5), constrained rewriting (3/5), tool calling (4/5), classification (4/5, both tied for 1st of 53), agentic planning (3/5), and multilingual (4/5). Neither model differentiates on these dimensions.

External math benchmarks (Epoch AI): Both models struggle with advanced mathematics. GPT-4o-mini scores 52.6% on MATH Level 5 (rank 13 of 14 models tested) and 6.9% on AIME 2025 (rank 21 of 23). Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 (rank 14 of 14 — last) and 5.1% on AIME 2025 (rank 23 of 23 — last). Neither model is appropriate for competition-level mathematics; GPT-4o-mini has a modest edge on MATH Level 5, but both scores fall well below the field median of 94.15% among models with external benchmark data.

GPT-4o-mini costs $0.15/M input tokens and $0.60/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — making it 33% cheaper on input and 47% cheaper on output. In practice, output cost dominates for most conversational and generation workloads. At 1M output tokens/month, you pay $600 for GPT-4o-mini vs $320 for Llama 3.3 70B Instruct — a $280 difference. At 10M tokens/month, that gap grows to $2,800. At 100M tokens/month, you're looking at $28,000 in savings by choosing Llama 3.3 70B Instruct. For consumer-facing applications with high throughput — chatbots, summarization pipelines, document processing — the cost differential is significant enough to be a primary decision factor. For low-volume or internal tools where the absolute spend is small, the $0.28/M output difference is less consequential than benchmark fit.

Choose Llama 3.3 70B Instruct if:

• You're running high-volume text workloads and the $0.28/M output token savings compounds meaningfully at your scale.
• Your application depends on retrieval-augmented generation, document summarization, or long-context tasks — it scores 5/5 on long context in our tests, tied for 1st of 55 models.
• Faithfulness to source material is critical (4 vs 3 in our testing, and GPT-4o-mini ranks near the bottom of the field at 52nd of 55).
• You need solid creative problem solving or strategic analysis relative to budget alternatives.
• You're working with text-only inputs and don't require image or file processing.

Choose GPT-4o-mini if:

• Your application requires image or file input — Llama 3.3 70B Instruct is text-only per the payload.
• Safety calibration is a hard requirement: GPT-4o-mini scores 4/5 vs Llama 3.3 70B Instruct's 2/5 in our testing, ranking 6th of 55 models.
• You're building a branded assistant or roleplay product where persona consistency matters (GPT-4o-mini scores 4 vs 3).
• You need the web_search_options or logit_bias parameters, which are present in GPT-4o-mini's supported parameters but not listed for Llama 3.3 70B Instruct.
• Your volume is low enough that the cost difference is negligible and you want the OpenAI ecosystem's tooling and API surface.