GPT-5.4 Mini vs Llama 3.3 70B Instruct

Across our 12-test benchmark suite (scored 1–5), GPT-5.4 Mini outperforms Llama 3.3 70B Instruct on 8 tests, ties on 4, and loses none.

Where GPT-5.4 Mini wins:

• Structured output (5 vs 4): GPT-5.4 Mini ties for 1st among 54 models tested; Llama 3.3 70B Instruct ranks 26th of 54. For JSON schema compliance and format adherence in production pipelines, GPT-5.4 Mini is the more reliable choice.

• Strategic analysis (5 vs 3): GPT-5.4 Mini ties for 1st among 54 models; Llama 3.3 70B Instruct ranks 36th of 54. This is a meaningful gap — nuanced tradeoff reasoning with real numbers is a task where Llama 3.3 70B Instruct falls well below the median (p50 = 4 across all models).

• Faithfulness (5 vs 4): GPT-5.4 Mini ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 34th of 55. When sticking to source material without hallucinating is critical — summarization, RAG, document Q&A — GPT-5.4 Mini has a measurable edge.

• Persona consistency (5 vs 3): GPT-5.4 Mini ties for 1st among 53 models; Llama 3.3 70B Instruct ranks 45th of 53 — near the bottom of the field. For chatbots, roleplay, or any application requiring stable character maintenance, this is a significant gap.

• Agentic planning (4 vs 3): GPT-5.4 Mini ranks 16th of 54; Llama 3.3 70B Instruct ranks 42nd of 54. Goal decomposition and failure recovery in multi-step agentic workflows strongly favors GPT-5.4 Mini.

• Multilingual (5 vs 4): GPT-5.4 Mini ties for 1st among 55 models; Llama 3.3 70B Instruct ranks 36th of 55. For non-English output quality, GPT-5.4 Mini is markedly stronger.

• Creative problem solving (4 vs 3): GPT-5.4 Mini ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th of 54. Non-obvious, feasible ideation favors GPT-5.4 Mini.

• Constrained rewriting (4 vs 3): GPT-5.4 Mini ranks 6th of 53; Llama 3.3 70B Instruct ranks 31st of 53. Compression within hard character limits is another area where GPT-5.4 Mini pulls ahead.

Where they tie:

• Tool calling (4 vs 4): Both rank 18th of 54, sharing the score with 28 other models. Function selection and argument accuracy are equivalent.

• Classification (4 vs 4): Both tie for 1st among 53 models. Routing and categorization tasks are equally well served by either model.

• Long context (5 vs 5): Both tie for 1st among 55 models. Note that GPT-5.4 Mini's context window is 400,000 tokens vs 131,072 for Llama 3.3 70B Instruct — so while retrieval accuracy is equal, GPT-5.4 Mini can handle significantly longer documents.

• Safety calibration (2 vs 2): Both rank 12th of 55. Neither model excels here relative to the field — the p75 across all models is only 2, so this reflects a general limitation of current models on this test.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 and 5.1% on AIME 2025, ranking last (14th of 14 and 23rd of 23 respectively) among models with external benchmark data. GPT-5.4 Mini has no external benchmark scores in our data payload. These results indicate Llama 3.3 70B Instruct is not competitive on advanced competition mathematics relative to other models in this comparison set.

The pricing gap here is substantial and worth modeling carefully. GPT-5.4 Mini charges $0.75 per million input tokens and $4.50 per million output tokens. Llama 3.3 70B Instruct charges $0.10 input and $0.32 output — a 7.5x input gap and a 14x output gap.

At 1M output tokens/month: GPT-5.4 Mini costs $4.50 vs $0.32 — a $4.18 difference, negligible for most applications.

At 10M output tokens/month: $45.00 vs $3.20 — a $41.80 monthly gap that starts to matter for growing products.

At 100M output tokens/month: $450.00 vs $32.00 — a $418/month difference that becomes a meaningful budget line item.

At 1B output tokens/month: $4,500 vs $320 — an $4,180/month gap that will dominate infrastructure decisions.

Developers running high-throughput pipelines — content generation, classification at scale, chatbots with millions of sessions — should weigh whether GPT-5.4 Mini's benchmark advantages (particularly in persona consistency, strategic analysis, faithfulness, and multilingual) justify a 14x output cost premium. For applications where classification and tool calling are the primary workloads — and both models score identically on those — Llama 3.3 70B Instruct is the clear value choice. GPT-5.4 Mini also supports image and file inputs, which Llama 3.3 70B Instruct does not (text only), so multimodal use cases may make the premium unavoidable.

Choose GPT-5.4 Mini if:

• You need strong persona consistency for chatbots or character-driven applications (scores 5 vs 3, ranking 1st vs 45th of 53).
• Your workflows involve strategic analysis, faithfulness to source material, or multilingual output — all areas where GPT-5.4 Mini scores 5 vs Llama 3.3 70B Instruct's 3 or 4.
• You need image or file inputs alongside text (GPT-5.4 Mini supports multimodal input; Llama 3.3 70B Instruct is text-only).
• You're building agentic systems requiring multi-step planning and failure recovery (ranks 16th vs 42nd of 54).
• You need a 400,000-token context window — over 3x larger than Llama 3.3 70B Instruct's 131,072.
• Volume is low-to-moderate and the 14x output cost premium is acceptable given quality requirements.

Choose Llama 3.3 70B Instruct if:

• Your primary workloads are classification, tool calling, or long-context retrieval — where both models score identically.
• You're running high-volume pipelines where $0.32/M output tokens versus $4.50/M output tokens produces meaningful cost savings (e.g., $418+/month at 100M output tokens).
• You want access to logprobs, top_k, min_p, repetition penalty, and other fine-grained sampling controls not available in GPT-5.4 Mini.
• You do not need image or file input support.
• Advanced math (competition-level) is not a use case — Llama 3.3 70B Instruct ranks last among externally benchmarked models on MATH Level 5 (41.6%) and AIME 2025 (5.1%) per Epoch AI.