Llama 4 Scout vs o4 Mini
o4 Mini is the better pick for most production use cases that require structured outputs, tool calling, multilingual and faithfulness — it wins 8 of 12 benchmarks in our testing. Llama 4 Scout is the sensible cost-first alternative: it wins safety calibration, ties on long-context and classification, and costs a fraction of o4 Mini.
meta-llama
Llama 4 Scout
Benchmark Scores
External Benchmarks
Pricing
Input
$0.080/MTok
Output
$0.300/MTok
modelpicker.net
openai
o4 Mini
Benchmark Scores
External Benchmarks
Pricing
Input
$1.10/MTok
Output
$4.40/MTok
modelpicker.net
Benchmark Analysis
Score-by-score in our 12-test suite: - Structured_output: o4 Mini 5 vs Llama 4 Scout 4 — o4 Mini wins and ranks “tied for 1st” (rank 1 of 54). This matters for JSON schema and format adherence in integrations. - Tool_calling: o4 Mini 5 vs Scout 4 — o4 Mini wins and is “tied for 1st” (rank 1 of 54), so it better selects functions and arguments in our tests. - Faithfulness: o4 Mini 5 vs Scout 4 — o4 Mini wins and ranks “tied for 1st” (rank 1 of 55), reducing hallucination risk in source-dependent tasks. - Persona_consistency: o4 Mini 5 vs Scout 3 — o4 Mini wins (tied for 1st), useful for chat assistants. - Multilingual: o4 Mini 5 vs Scout 4 — o4 Mini wins (tied for 1st), so non‑English outputs were stronger. - Agentic_planning: o4 Mini 4 vs Scout 2 — o4 Mini wins (rank 16 of 54), better at goal decomposition in our tests. - Creative_problem_solving: o4 Mini 4 vs Scout 3 — o4 Mini wins (rank 9 of 54), useful for novel suggestions. - Strategic_analysis: o4 Mini 5 vs Scout 2 — o4 Mini wins and is tied for top (rank 1 of 54), important for nuanced tradeoffs. - Constrained_rewriting: tie 3 vs 3 — both matched on compression within hard limits. - Classification: tie 4 vs 4 — both tied for 1st among many models. - Long_context: tie 5 vs 5 — both tied for 1st; note Scout has a larger context_window (327,680 vs o4 Mini 200,000) and ranks “tied for 1st” in our long-context test. - Safety_calibration: Llama 4 Scout 2 vs o4 Mini 1 — Scout wins here (Scout rank 12 of 55 vs o4 Mini rank 32 of 55), so Scout refused harmful prompts more consistently in our testing. External math signals for o4 Mini: MATH Level 5 97.8% and AIME 2025 81.7% (Epoch AI), where o4 Mini ranks highly (MATH Level 5 rank 2 of 14; AIME 2025 rank 13 of 23). Llama 4 Scout has no external math scores in the payload. Overall, o4 Mini dominates 8 categories; Scout wins safety calibration and ties three categories.
Pricing Analysis
Per-mTok pricing: Llama 4 Scout input $0.08 / output $0.30; o4 Mini input $1.10 / output $4.40. Assuming a 50/50 split of input/output tokens, monthly costs: 1M tokens (1,000 mTok) => Llama 4 Scout $190 vs o4 Mini $2,750; 10M tokens => Scout $1,900 vs o4 Mini $27,500; 100M tokens => Scout $19,000 vs o4 Mini $275,000. The cost gap (o4 Mini ≈14–15× more expensive at these mixes) matters for high-volume chat, summarization, or API-heavy products; small teams or research projects may accept o4 Mini's higher cost for its stronger benchmarked quality, while startups and high-throughput services should prefer Llama 4 Scout to control cloud spend.
Real-World Cost Comparison
Bottom Line
Choose o4 Mini if you need best-in-suite structured output, tool-calling, faithfulness, multilingual/chat consistency, or top-tier strategic analysis — our testing shows o4 Mini wins 8 of 12 benchmarks and posts top ranks across those categories. Choose Llama 4 Scout if you are cost-sensitive or prioritize safety calibration and extremely large context windows (Scout: 327,680 tokens vs o4 Mini: 200,000) — Scout cuts token bills by ≈14× at typical mixes and still ties on long-context and classification.
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.