Why Compare All Meta Models Together
The Compare Prices tool is the fastest way to put a shortlist of LLMs in a single grid — input cost, output cost, context window, and quality score in stacked columns you can scan vertically. Provider dropdowns let you mix models across Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and xAI without leaving the page. Picking between Meta's models — flagship, mid-tier, fast — is usually done by reading the provider's pricing page top-to-bottom, which buries the spread. The Compare Prices grid flips that: you check **every Meta model** in one dropdown and the grid lays them out side-by-side. Input cost across the lineup spans $0.200 to $0.500 per 1M tokens — a 2.5× spread you can scan in three seconds. This guide walks through the lineup model-by-model with the framing of "what do you give up to step down a tier." Pair this with flagship/balanced/fast/reasoning LLM tiers, Arena AI leaderboard Elo scores explained, and how LLM quality scores are calculated.
The Meta Lineup, Top to Bottom
**Llama 4 Maverick** — $0.500 input / $1.50 output per 1M, 1M context, quality 70, tier **balanced**. balanced tier is the production-default zone — quality high enough for customer traffic, price low enough to scale. **Llama 4 Scout** — $0.200 input / $0.600 output per 1M, 1M context, quality 60, tier **fast**. fast tier is built for high-volume throughput at the lowest per-token rate the provider offers. All five attributes (input, output, context, quality, tier) live in the Compare Prices grid, which makes the cross-tier deltas obvious. Stepping from Llama 4 Maverick (Q70, $0.500) down to Llama 4 Scout (Q60, $0.200) saves 60% on input at a cost of 10 quality points — the right tradeoff if your workload tolerates the quality drop.
Where Each Meta Model Earns Its Place
**Llama 4 Maverick**: best for production routing default — chatbots, RAG answer synthesis, structured output, anything that ships to real users at scale. **Llama 4 Scout**: best for high-volume classification, lightweight summarization, embeddings-adjacent tasks, prefilters and triage stages, draft generation. This isn't marketing copy — it's how the tier classification on TokenRate's filter panel actually slots them. If you've already filtered to a tier, the Compare Prices grid is the next step: check the relevant Meta models alongside their cross-provider peers (e.g., Llama 4 Scout next to Gemini Flash, or Llama 4 Maverick next to GPT-5 and Grok 4) to confirm you're not paying a provider premium.
Cost Multipliers When Stepping Up a Tier
Within Meta's lineup, the step-up multipliers are stark. Input: Llama 4 Scout → Llama 4 Maverick is 2.5×. Output: 2.5×. Quality: +10 points. The question to ask: is +10 quality points worth 2.5× the per-token cost? For agentic or accuracy-critical workloads, yes — quality wins are non-linear in user value. For high-volume classification or templated content, no — the cheaper model clears the bar. Use the API cost estimator to put a dollar figure on the step-up at your workload volume.
Compare-Prices Across Providers, Not Just Within
The most common mistake when picking within Meta's lineup is forgetting that cross-provider competitors may dominate the chosen tier. Once you've picked your Meta candidates, add 1-2 competitors from a different provider dropdown — for the balanced tier, comparing Meta's mid-model against Gemini 2.5 Pro and Claude Sonnet 4.7 is usually instructive. The Compare Prices grid was designed for exactly this multi-provider workflow. Both the price denominator (OpenRouter) and the quality numerator (Arena AI + Artificial Analysis) refresh hourly. So the comparison you screenshot Monday morning is still trustworthy at standup Tuesday morning — but you should re-run it before a quarterly model-routing review. Run the comparison live at /tools/compare-prices, then bookmark the URL for next month's price audit.