Claude Haiku 4.5 vs Llama 4 Maverick

Head-to-head by test (scores are our 1–5 ratings):

• Strategic analysis: Claude Haiku 4.5 5 vs Llama 4 Maverick 2 — Haiku wins and in our rankings is tied for 1st of 54 models, meaning it handles nuanced tradeoff reasoning and numeric tradeoffs much better in practice.
• Creative problem solving: 4 vs 3 — Haiku wins; expect more non-obvious, feasible ideas from Haiku in our tests (rank 9 of 54 for Haiku vs rank 30 for Maverick).
• Tool calling: 5 vs (rate-limited/quasi-failed) — Haiku scored 5 and is tied for 1st on tool calling; Maverick’s tool_calling test encountered a 429 rate limit on OpenRouter (payload quirk), so Haiku is the reliable winner for function selection, argument accuracy, and sequencing in our testing.
• Faithfulness: 5 vs 4 — Haiku wins and ranks tied for 1st (stays closer to source material; fewer hallucinations on tasks we ran).
• Classification: 4 vs 3 — Haiku wins and is tied for 1st; expect better routing and categorization accuracy in our suite.
• Long context: 5 vs 4 — Haiku wins in our tests (tied for 1st by rank), delivering better retrieval accuracy over 30K+ tokens despite Maverick’s larger raw context window (Maverick: 1,048,576; Haiku: 200,000).
• Agentic planning: 5 vs 3 — Haiku wins (tied for 1st), producing stronger decomposition and failure-recovery behavior.
• Multilingual: 5 vs 4 — Haiku wins (tied for 1st), giving higher-quality non-English output in our runs.
• Structured output: 4 vs 4 — tie; both models meet JSON/schema adherence similarly (rank 26 of 54 for both).
• Constrained rewriting: 3 vs 3 — tie; both handle compression within tight limits at similar levels.
• Persona consistency: 5 vs 5 — tie; both resist injection and maintain character equally well (tied for 1st).
• Safety calibration: 2 vs 2 — tie; both models show similar refusal/permission behavior in our safety tests. Summary: Claude Haiku 4.5 wins 8 benchmarks, Llama 4 Maverick wins 0, and 4 tests tie. Rankings show Haiku often sits at or near the top (multiple tied-for-1st ranks), whereas Maverick typically ranks mid-table on the same tests (e.g., strategic_analysis rank 44 of 54). These differences translate to noticeably better reasoning, tool use, faithfulness, and multilingual quality from Haiku in our suite, at a substantial cost premium.

Raw rates from the payload: Claude Haiku 4.5 charges $1.00 per mTok input and $5.00 per mTok output; Llama 4 Maverick charges $0.15 per mTok input and $0.60 per mTok output. That makes Haiku's output token cost 8.33x higher ($5.00/$0.60 = 8.3333, from priceRatio). Example monthly costs assuming a 50/50 split of input vs output tokens (i.e., half of tokens are prompts, half are generations):

• 1M tokens/month -> 500 mToks input + 500 mToks output: Haiku = $500 + $2,500 = $3,000; Maverick = $75 + $300 = $375.
• 10M tokens/month -> Haiku = $30,000; Maverick = $3,750.
• 100M tokens/month -> Haiku = $300,000; Maverick = $37,500. Who should care: startups and high-volume applications (≥10M tokens/month) will see large absolute savings with Llama 4 Maverick; teams prioritizing top benchmark performance, tool calling accuracy, or highest faithfulness may accept Haiku’s higher costs. Note the payload’s priceRatio (8.33x) refers to output-token pricing specifically.

Choose Claude Haiku 4.5 if you need top-tier performance on strategic analysis, tool calling, faithfulness, long-context tasks, or multilingual production workloads and you can absorb higher inference costs. Choose Llama 4 Maverick if budget and token efficiency are critical, you need a very large context window (1,048,576 tokens), or you must serve large volumes cost-effectively — Maverick’s per-mTok rates ($0.15 input / $0.60 output) make it far cheaper at scale.