Meta Llama
meta-llama appears in our benchmarks as a budget-focused Llama provider with a small lineup. In our testing it delivers low output cost (Llama 4 Scout at $0.30) and mid-range average performance (3.33/5). Target audience: developers and teams chasing low inference costs and simple Llama-based deployments who are willing to trade some benchmark performance versus top-tier providers.
Models
0
Cheapest Output
N/A
Avg Score
0.00/5
Price Range
N/A
Model Lineup
Models present in the payload tied to meta-llama include Llama 4 Scout, Llama 4 Maverick, and Llama 3.3 70B Instruct. Our competitorComparison identifies Llama 4 Scout as meta-llama's best model in our testing (avgScore 3.333/5) and lists an output cost of $0.30. The payload does not provide per-model pricing for Llama 4 Maverick or Llama 3.3 70B Instruct under meta-llama; providerStats shows no detailed model pricing. Practical guidance: use Llama 4 Scout when output cost is the primary constraint — the payload lists $0.30 output cost; evaluate Llama 4 Maverick or Llama 3.3 70B Instruct only after confirming pricing and running targeted tests, because the payload does not include their costs or per-test breakdowns.
Strengths and Weaknesses
Strengths — Cost: in our testing meta-llama's Llama 4 Scout lists an output cost of $0.30, making it one of the lowest-cost options in the competitor set. Simplicity: the payload shows a compact lineup, which can simplify procurement and deployment choices. Weaknesses — Benchmark performance: meta-llama's average score is 3.333/5 in our 12-test suite, below competitors such as Anthropic (4.667/5), OpenAI (4.667/5), Google (4.5/5), and DeepSeek (4.5/5). That gap means trade-offs in capability: if the workload depends on top-tier reasoning, coding, or safety calibration per our benchmarks, higher-scoring providers may be preferable despite higher cost. Transparency gap — the payload contains only one explicit price point for meta-llama (Llama 4 Scout $0.30) and no provider-level modelCount or detailed per-model prices, so teams should run their own trials and request full pricing/benchmarks before committing.
Pricing
Compared to competitors in the payload, meta-llama is a budget provider on output cost. Competitor outputCosts in our data: Anthropic (Opus/Sonnet) $15, OpenAI (GPT-5.2) $14, Google (Gemini 3 Flash Preview) $3, DeepSeek R1 0528 $2.15, X.ai (Grok 4.20) $6, Mistral Medium 3.1 $2, Meta (Llama 3.3 70B Instruct) $0.32, and meta-llama (Llama 4 Scout) $0.30. With $0.30 outputCost for Llama 4 Scout, meta-llama sits in the budget tier for runtime pricing; however average benchmark performance (3.333/5) is below higher-cost providers like Anthropic and OpenAI (4.667/5 each in our comparison). Note: providerStats lacks a full price table for meta-llama models in the payload, so per-model input/output rates beyond Llama 4 Scout are not available here.
Pricing vs Performance
Output cost per million tokens (log scale) vs average score across our 12 internal benchmarks
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.