GPT-5.1 vs Llama 3.3 70B Instruct

Head-to-head by our 12-test suite: GPT-5.1 wins 7 tests, Llama 3.3 70B Instruct wins 0, and 5 tests tie (winLossTie). Wins (GPT-5.1): strategic analysis 5 vs 3 (ranks: GPT-5.1 tied for 1st on strategic analysis in our rankingsA display; Llama ranks 36 of 54), constrained rewriting 4 vs 3 (GPT-5.1 rank 6 of 53; Llama rank 31), creative problem solving 4 vs 3 (GPT-5.1 rank 9 of 54; Llama rank 30), faithfulness 5 vs 4 (GPT-5.1 tied for 1st with 32 others out of 55; Llama rank 34), persona consistency 5 vs 3 (GPT-5.1 tied for 1st; Llama rank 45), agentic planning 4 vs 3 (GPT-5.1 rank 16 of 54; Llama rank 42), multilingual 5 vs 4 (GPT-5.1 tied for 1st; Llama rank 36). Ties (no clear winner): structured output 4 vs 4 (both rank 26 of 54), tool calling 4 vs 4 (both rank 18 of 54), classification 4 vs 4 (both tied for 1st with many models), long context 5 vs 5 (both tied for 1st), safety calibration 2 vs 2 (both rank 12 of 55). What this means: GPT-5.1 will perform better on tasks requiring nuanced tradeoff reasoning, constrained compression, faithful use of source material, strong persona maintenance, multilingual parity, and higher-level planning; these wins also place it near the top of our pool on those axes. Llama 3.3 70B Instruct holds parity on schema/JSON output, tool selection/arguments, classification, long-context retrieval, and safety calibration — so for structured automation, long-context retrieval, and function/tool pipelines Llama is effectively competitive. External benchmark context: GPT-5.1 scores 68 on SWE-bench Verified (Epoch AI) and 88.6 on AIME 2025 (Epoch AI); Llama 3.3 70B Instruct reports 41.6 on MATH Level 5 and 5.1 on AIME 2025 (Epoch AI). Per Epoch AI results, GPT-5.1 shows materially stronger performance on these math/coding/olympiad-style third-party tests.

Per-token rates: GPT-5.1 charges $1.25 per 1k input tokens and $10 per 1k output tokens; Llama 3.3 70B Instruct charges $0.10 per 1k input and $0.32 per 1k output. If you produce equal input and output volume, cost per combined 1M input+1M output tokens is $11.25 for GPT-5.1 and $0.42 for Llama 3.3 70B Instruct. At 10M in+10M out monthly: GPT-5.1 ≈ $112.50 vs Llama ≈ $4.20. At 100M in+100M out monthly: GPT-5.1 ≈ $1,125 vs Llama ≈ $42. The payload shows a priceRatio of 31.25, i.e., GPT-5.1 is ~31× more expensive by these per-mtok rates. Teams with large volume (10M+ tokens/month), cost-sensitive products, or lightweight on-prem workflows should favor Llama 3.3 70B Instruct. Enterprises that need the highest faithfulness, strategic reasoning, and stronger external-math/coding bench evidence (see benchmark_analysis) may justify GPT-5.1's premium.

Choose GPT-5.1 if you need best-in-class faithfulness, strategic reasoning, multilingual parity, persona consistency, and superior performance on external math/coding benchmarks (e.g., SWE-bench 68; AIME 88.6), and you can absorb $10/mtok output pricing. Choose Llama 3.3 70B Instruct if you must minimize runtime costs (output $0.32/mtok), need parity on structured output, long-context, classification, or tool-calling, and you can accept lower scores on creative problem solving, strategic analysis, persona consistency and external math benchmarks.