Llama 3.3 70B Instruct vs Llama 4 Scout

We ran both models across our 12-test suite and compare scores and ranks below (all scores are from our testing). Summary: Llama 3.3 70B Instruct wins 2 tests, Llama 4 Scout wins 0, and 10 tests are ties. Detailed walk-through: - strategic analysis (nuanced tradeoff reasoning): Instruct 3 vs Scout 2 — Instruct wins; Instruct ranks 36 of 54 (8 models share score), Scout ranks 44 of 54 (11 models share score). This matters when tasks need numeric tradeoffs or cost/benefit reasoning. - agentic planning (goal decomposition & recovery): Instruct 3 vs Scout 2 — Instruct wins; Instruct rank 42 of 54 (11 models share score), Scout rank 53 of 54 (2 models share score). Expect better high-level plan decomposition from Instruct in our tests. - structured output (JSON/format adherence): Instruct 4 vs Scout 4 — tie; both rank 26 of 54 (27 models share this score). Good for schema-constrained outputs in both models. - constrained rewriting (tight character limits): 3 vs 3 — tie; both rank 31 of 53 (22 models share). - creative problem solving (non-obvious ideas): 3 vs 3 — tie; both rank 30 of 54 (17 models share). - tool calling (function choice & args): 4 vs 4 — tie; both rank 18 of 54 (29 models share). For function selection and sequencing both performed similarly in our tests. - faithfulness (sticking to sources): 4 vs 4 — tie; both rank 34 of 55 (18 models share). - classification (routing/categorization): 4 vs 4 — tie; both are tied for 1st with 29 other models out of 53 tested (tied top performers in our suite). - long context (retrieval at 30K+ tokens): 5 vs 5 — tie; both tied for 1st with 36 others out of 55. Note: Scout has the larger raw context_window (327,680 vs 131,072) in the payload, which can matter beyond the test score ceiling. - safety calibration (refusal/allow): 2 vs 2 — tie; both rank 12 of 55 (20 models share). - persona consistency: 3 vs 3 — tie; both rank 45 of 53 (6 models share). - multilingual: 4 vs 4 — tie; both rank 36 of 55 (18 models share). Additional math notes: Llama 3.3 70B Instruct has math test results in our data — MATH Level 5 41.6 and AIME 2025 5.1 — and ranks last on those specific math lists (MATH Level 5 rank 14 of 14; AIME 2025 rank 23 of 23). Llama 4 Scout has no MATH Level 5 / AIME 2025 scores in this payload. In short: Instruct's measurable advantages are in strategic analysis and agentic planning; on most applied metrics both models tie in our suite. Scout brings multimodal input and a larger context window and is slightly cheaper per token.

Per the payload, Llama 3.3 70B Instruct charges $0.10 input / $0.32 output per mtoken; Llama 4 Scout charges $0.08 input / $0.30 output per mtoken. (The payload priceRatio is 1.0667, i.e., model A is modestly more expensive on average.) If you assume per-mtoken = per 1,000 tokens, per-million-token costs are: Llama 3.3 70B Instruct = $100 input / $320 output = $420 combined per 1M tokens; Llama 4 Scout = $80 input / $300 output = $380 combined per 1M tokens. With a 50/50 input/output split the totals are: for 1M tokens — $210 (Instruct) vs $190 (Scout); 10M tokens — $2,100 vs $1,900; 100M tokens — $21,000 vs $19,000. Put simply, Scout saves about $20 per 1M tokens on a balanced I/O workload and about $2,000 per 100M tokens. Teams with high-volume deployments (10M+ tokens/month) or tight cost targets should prioritize Scout; teams that need the small but measurable edge in planning/strategy may accept Instruct's higher cost.

Choose Llama 3.3 70B Instruct if: - You prioritize planning, decomposition, or nuanced tradeoff reasoning (it wins strategic analysis 3 vs 2 and agentic planning 3 vs 2 in our tests). - Your workflows are text-first and you value the small performance edge even at modestly higher cost. Choose Llama 4 Scout if: - You need multimodal inputs (text+image->text as listed in the payload) or the largest context window (327,680 tokens in the payload). - You run high-volume deployments or are cost-sensitive — Scout is cheaper per token ($0.08/$0.30 vs $0.10/$0.32). If you need schema adherence, tool calling, classification, long-context retrieval, or faithful outputs, both models performed similarly in our 12-test suite.