Llama 3.3 70B Instruct vs Llama 4 Maverick
For most text-first, cost-sensitive production workloads, Llama 3.3 70B Instruct is the better pick — it wins more benchmarks (4 vs 1) and is materially cheaper per token. Llama 4 Maverick wins persona consistency and adds multimodal (image) input, so pick Maverick when character consistency or vision input are primary requirements despite higher cost.
meta
Llama 3.3 70B Instruct
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.320/MTok
modelpicker.net
meta
Llama 4 Maverick
Benchmark Scores
External Benchmarks
Pricing
Input
$0.150/MTok
Output
$0.600/MTok
modelpicker.net
Benchmark Analysis
Head-to-head outcomes from our 12-test suite: Llama 3.3 70B Instruct wins strategic analysis (3 vs 2; A ranks 36 of 54 vs B rank 44 of 54), tool calling (A score 4; B hit a rate limit during our tool calling test), classification (A 4 vs B 3; A is tied for 1st with 29 others), and long context (A 5 vs B 4; A tied for 1st with 36 others). Llama 4 Maverick wins persona consistency (B 5 vs A 3; B tied for 1st with 36 others), which matters for systems that must maintain character or resist prompt injection. The rest of the suite ties: structured output (both 4), constrained rewriting (both 3), creative problem solving (both 3), faithfulness (both 4), safety calibration (both 2), agentic planning (both 3), and multilingual (both 4). Contextual implications: A’s long context 5 means stronger retrieval and accuracy at 30K+ tokens in our tests; its tool calling 4 and classification 4 indicate more reliable function selection and routing (A’s tool calling ranks 18 of 54). B’s persona consistency 5 shows it better preserves role/character in dialog (B tied for 1st). Note also Llama 3.3 reports MATH Level 5 = 41.6% and AIME 2025 = 5.1% in our captured external-style metrics (these are modelA values in the payload and should be interpreted as supplemental results).
Pricing Analysis
Per-mTok pricing from the payload: Llama 3.3 70B Instruct charges $0.10 input / $0.32 output; Llama 4 Maverick charges $0.15 input / $0.60 output. Assuming a 50/50 split of input/output tokens, a 1M-token month (500k input + 500k output = 500 mTok each) costs $210 for Llama 3.3 (500*$0.10 + 500*$0.32 = $50 + $160) and $375 for Llama 4 Maverick (500*$0.15 + 500*$0.60 = $75 + $300). At 10M tokens/month multiply those totals: $2,100 vs $3,750. At 100M tokens/month: $21,000 vs $37,500. The cost gap grows linearly: at 100M tokens you pay $16,500 more monthly for Maverick under this usage split. Teams with heavy-volume inference (10M+ tokens/month) or tight margins should prefer Llama 3.3; teams that need Maverick’s multimodal input or its higher persona consistency may accept the higher bill.
Real-World Cost Comparison
Bottom Line
Choose Llama 3.3 70B Instruct if you need: cost-efficient production inference at scale, superior long-context handling, stronger tool-calling and classification in our tests, or primarily text-only workloads. Choose Llama 4 Maverick if you need: multimodal (image→text) input or best-in-class persona consistency (character preservation) and are willing to pay roughly 1.8x–1.9x the token cost in typical usage.
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.