GPT-5.4 vs Llama 3.3 70B Instruct

Across our 12-test internal suite, GPT-5.4 outscores Llama 3.3 70B Instruct on 9 benchmarks, ties on 2, and loses on 1.

Where GPT-5.4 leads:

• Agentic planning: 5 vs 3. GPT-5.4 is tied for 1st among 54 models tested; Llama 3.3 70B Instruct ranks 42nd of 54. This is a meaningful gap — agentic planning tests goal decomposition and failure recovery, critical for multi-step AI workflows.
• Safety calibration: 5 vs 2. GPT-5.4 is tied for 1st among just 5 of 55 models — a rare top score. Llama 3.3 70B Instruct ranks 12th of 55 with a score of 2, well below the 50th percentile (p50 = 2). For applications where refusal accuracy matters, GPT-5.4 has a clear edge.
• Strategic analysis: 5 vs 3. GPT-5.4 tied for 1st of 54; Llama 3.3 70B Instruct ranks 36th. This reflects nuanced tradeoff reasoning — relevant for business analysis, research, and decision support tasks.
• Faithfulness: 5 vs 4. GPT-5.4 tied for 1st of 55; Llama 3.3 70B Instruct ranks 34th of 55. Staying grounded in source material is important for RAG pipelines and document-based Q&A.
• Persona consistency: 5 vs 3. GPT-5.4 tied for 1st of 53; Llama 3.3 70B Instruct ranks 45th. A large gap relevant to chatbot and customer-facing AI applications.
• Multilingual: 5 vs 4. GPT-5.4 tied for 1st of 55; Llama 3.3 70B Instruct ranks 36th. Both above the p50 (5), but GPT-5.4 holds the top score.
• Structured output: 5 vs 4. GPT-5.4 tied for 1st of 54; Llama 3.3 70B Instruct ranks 26th. JSON schema adherence matters for API integrations and data pipelines.
• Constrained rewriting: 4 vs 3. GPT-5.4 ranks 6th of 53; Llama 3.3 70B Instruct ranks 31st.
• Creative problem solving: 4 vs 3. GPT-5.4 ranks 9th of 54; Llama 3.3 70B Instruct ranks 30th.

Where it's tied:

• Tool calling: Both score 4, both rank 18th of 54 with 29 models sharing the score. No meaningful difference here.
• Long context: Both score 5, both tied for 1st of 55 with 36 other models. Equal performance at 30K+ token retrieval.

Where Llama 3.3 70B Instruct leads:

• Classification: 4 vs 3. Llama 3.3 70B Instruct is tied for 1st of 53 with 30 models; GPT-5.4 ranks 31st of 53. For routing and categorization workloads, Llama 3.3 70B Instruct is the better (and far cheaper) choice.

External benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested), placing it among the top coding models by that external measure. On AIME 2025, GPT-5.4 scores 95.3% (rank 3 of 23), well above the p75 of 90%. Llama 3.3 70B Instruct scores 5.1% on AIME 2025 (rank 23 of 23 — last among models tested) and 41.6% on MATH Level 5 (rank 14 of 14 — also last among models with that score). These external results confirm a substantial gap in advanced reasoning and coding capability. Note that GPT-5.4 does not have a MATH Level 5 score in the payload, and Llama 3.3 70B Instruct does not have a SWE-bench Verified score.

The pricing gap here is extreme: GPT-5.4 costs $2.50 per million input tokens and $15.00 per million output tokens; Llama 3.3 70B Instruct costs $0.10 input and $0.32 output — a 46.9x ratio on output. At 1M output tokens/month, you're paying $15 vs $0.32 — nearly identical at this scale. At 10M output tokens/month, that's $150 vs $3.20 — a $146.80 monthly difference that starts to matter for bootstrapped teams. At 100M output tokens/month, GPT-5.4 runs $1,500 vs $32 — a $1,468 monthly gap that is a significant infrastructure line item for any business. Developers running high-volume pipelines — content generation, summarization, classification at scale — should take the cost difference seriously. For low-volume, high-stakes tasks like agentic workflows or complex analysis, GPT-5.4's performance advantages are likely worth the premium.

Choose GPT-5.4 if: You're building agentic pipelines, multi-step autonomous workflows, or applications where safety calibration and faithfulness to source material are non-negotiable. Also the right call for complex analysis, non-English language support, persona-driven chatbots, and any coding or math-intensive work — its 76.9% SWE-bench Verified score (Epoch AI, rank 2 of 12) and 95.3% AIME 2025 score (rank 3 of 23) put it in a different class for those tasks. The cost is real — $15.00/M output tokens — but justified when quality drives outcomes.

Choose Llama 3.3 70B Instruct if: Your primary workload is classification, text routing, or categorization — where it ties for 1st in our testing at a fraction of the cost. Also the right choice for high-volume, cost-sensitive deployments where the task complexity doesn't demand frontier-model capabilities: at $0.32/M output tokens, you can run roughly 47x the volume for the same budget. Teams self-hosting open-weight models or optimizing inference cost at scale should model this out carefully before defaulting to the premium option.