GPT-5.2 vs Llama 3.3 70B Instruct
GPT-5.2 is the better pick for high-stakes, long-context and agentic workflows — it wins 8 of 12 internal benchmarks (safety, strategy, faithfulness, etc.) and leads on third‑party math/AIME tests. Llama 3.3 70B Instruct ties on long-context, classification and tool calling but is dramatically cheaper, so pick it when cost and text-only inference dominate.
openai
GPT-5.2
Benchmark Scores
External Benchmarks
Pricing
Input
$1.75/MTok
Output
$14.00/MTok
modelpicker.net
meta
Llama 3.3 70B Instruct
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.320/MTok
modelpicker.net
Benchmark Analysis
Our comparison uses the 12-test internal suite (scores 1–5) plus external benchmarks where provided. Summary: GPT-5.2 wins 8 internal tests, Llama wins none, and 4 tests tie. Detailed walk-through (scores shown as GPT-5.2 vs Llama 3.3 70B Instruct):
- Strategic analysis: 5 vs 3 — GPT-5.2 tied for 1st ("tied for 1st with 25 other models out of 54") in our ranking; this means better nuanced tradeoff reasoning for planning and numeric tradeoffs.
- Constrained rewriting: 4 vs 3 — GPT-5.2 ranks 6th of 53; better at tight compression/strict length limits.
- Creative problem solving: 5 vs 3 — GPT-5.2 tied for 1st; stronger at non-obvious, feasible idea generation.
- Faithfulness: 5 vs 4 — GPT-5.2 tied for 1st (stays closer to source material, fewer hallucinations in our tests).
- Safety calibration: 5 vs 2 — GPT-5.2 tied for 1st; Llama scores lower here, so GPT-5.2 refuses harmful requests more reliably in our testing.
- Persona consistency: 5 vs 3 — GPT-5.2 tied for 1st; better at maintaining role/character and resisting injection.
- Agentic planning: 5 vs 3 — GPT-5.2 tied for 1st; stronger goal decomposition and recovery in our tests.
- Multilingual: 5 vs 4 — GPT-5.2 tied for 1st; higher non-English parity in our suite. Ties (equal performance): structured output 4 vs 4 (JSON/schema compliance), tool calling 4 vs 4 (function selection and sequencing), classification 4 vs 4 (both tied for 1st with many models), long context 5 vs 5 (both tied for 1st on retrieval at 30K+ tokens). Rankings confirm GPT-5.2 occupies the top positions in many categories (multiple "tied for 1st" displays), while Llama's strengths are concentrated in classification and long-context parity. External benchmarks (Epoch AI): GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI), ranking 5 of 12 — supporting strong coding ability; GPT-5.2 scores 96.1% on AIME 2025 (Epoch AI), ranking 1 of 23. Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 (Epoch AI) and 5.1% on AIME 2025 (Epoch AI), ranking last on those external math benchmarks. Note: external percentages are Epoch AI results; internal 1–5 scores are our testing.
Pricing Analysis
Raw per-1k-token pricing (input + output) from the payload: GPT-5.2 = $1.75 + $14.00 = $15.75 per 1k tokens; Llama 3.3 70B Instruct = $0.10 + $0.32 = $0.42 per 1k tokens. At monthly volumes: 1M tokens = 1,000k → GPT-5.2 $15,750 vs Llama $420; 10M tokens → GPT-5.2 $157,500 vs Llama $4,200; 100M tokens → GPT-5.2 $1,575,000 vs Llama $42,000. The ~43.75x priceRatio in the payload means GPT-5.2 is only sensible for use cases where its higher scores (safety, strategic analysis, agentic planning, AIME performance and broader modality/context support) justify enterprise-scale spend. Teams building high-volume, cost-sensitive products should prefer Llama 3.3 70B Instruct; teams needing the highest fidelity, safety and agentic capability should budget for GPT-5.2.
Real-World Cost Comparison
Bottom Line
Choose GPT-5.2 if: you need best-in-class safety calibration, strategic/agentic planning, faithfulness, creative problem solving, multimodal input (text+image+file->text), and top AIME/SWE-bench performance — and you can absorb $15.75/1k tokens. Choose Llama 3.3 70B Instruct if: you need a text-only model with equal classification and long-context behavior at massive cost savings (about $0.42/1k tokens), or you’re running very high token volumes where price dominates the decision.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.