GPT-4o vs Llama 4 Scout
For most developers and chat-first products that prioritize persona fidelity and agentic planning, GPT-4o is the stronger pick in our tests. Llama 4 Scout wins on long-context retrieval and safety calibration and is far cheaper — choose it when 30K+ token context and cost-per-token matter.
openai
GPT-4o
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$10.00/MTok
modelpicker.net
meta-llama
Llama 4 Scout
Benchmark Scores
External Benchmarks
Pricing
Input
$0.080/MTok
Output
$0.300/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite the run-down is: GPT-4o wins persona consistency (5 vs 3; GPT-4o tied for 1st with 36 others, Llama 4 Scout ranks 45/53) and agentic planning (4 vs 2; GPT-4o ranks 16/54, Llama 4 Scout ranks 53/54). Llama 4 Scout wins long context (5 vs 4; Llama 4 Scout tied for 1st with 36 others, GPT-4o ranks 38/55) and safety calibration (2 vs 1; Llama 4 Scout rank 12/55, GPT-4o rank 32/55). The remaining eight tests are ties: structured output (4/4), strategic analysis (2/2), constrained rewriting (3/3), creative problem solving (3/3), tool calling (4/4), faithfulness (4/4), classification (4/4), and multilingual (4/4) — both models deliver comparable performance there. Practical interpretation: GPT-4o’s higher persona consistency and agentic planning scores mean stronger behavior stability for character-driven chatbots and better task decomposition/failure recovery for agentic workflows. Llama 4 Scout’s long context top score translates to more reliable retrieval and reference when working with 30K+ token contexts. Safety_calibration favoring Llama 4 Scout indicates it refused or handled harmful prompts more appropriately in our tests. Note external benchmarks available for GPT-4o: SWE-bench Verified = 31% (Epoch AI), Math Level 5 = 53.3% (Epoch AI) and AIME 2025 = 6.4% (Epoch AI) — we report these as supplementary external measures. Llama 4 Scout has no external scores in this payload.
Pricing Analysis
GPT-4o output costs $10 per mtok and input $2.50 per mtok; Llama 4 Scout output costs $0.30 per mtok and input $0.08 per mtok. Using mtok = 1,000 tokens, output-only monthly costs are: GPT-4o = $10,000 (1M tokens), $100,000 (10M), $1,000,000 (100M); Llama 4 Scout = $300, $3,000, $30,000. If you pay for input+output (50/50 split), GPT-4o = $12,500 (1M), $125,000 (10M), $1,250,000 (100M); Llama 4 Scout = $380, $3,800, $38,000. The output cost ratio (GPT-4o : Llama 4 Scout) is ~33.33x (payload priceRatio). High-volume APIs, startups, and anyone serving millions of tokens/month should care — the choice can mean six- to seven-figure differences at scale. Low-volume projects that need GPT-4o’s persona/planning strengths can justify the premium; cost-sensitive or large-scale retrieval use cases should favor Llama 4 Scout.
Real-World Cost Comparison
Bottom Line
Choose GPT-4o if you need: high persona consistency and stronger agentic planning for chatbots, assistants, or agent-style workflows and can absorb a steep price premium (output $10/mtok). Choose Llama 4 Scout if you need: cost-effective production at scale, best-in-test long-context retrieval (30K+ tokens) and better safety calibration in our testing — it’s the practical choice when tokens are measured in millions.
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.