Claude Opus 4.7 vs Llama 4 Maverick

Across our 12-test benchmark suite, Claude Opus 4.7 outperforms Llama 4 Maverick on 8 tests and ties on 4. Llama 4 Maverick wins none outright.

Where Claude Opus 4.7 wins decisively:

• Tool calling: Opus 4.7 scores 5/5 (tied for 1st among 55 models); Llama 4 Maverick's tool calling result is missing from our data due to a rate limit encountered during testing, so no score comparison is available on this dimension. Note the quirk: our test hit a 429 rate limit on Llama 4 Maverick during the tool calling evaluation — likely transient, but the gap in documented reliability matters for production agentic systems.

• Agentic planning: Opus 4.7 scores 5/5 (tied for 1st among 55 models); Llama 4 Maverick scores 3/5 (ranked 43rd of 55). This is a meaningful gap. Agentic planning tests goal decomposition and failure recovery — the skills that determine whether a model can reliably drive multi-step autonomous workflows. A 5 vs 3 here has real consequences for anyone building agents.

• Strategic analysis: Opus 4.7 scores 5/5 (tied for 1st among 55 models); Llama 4 Maverick scores 2/5 (ranked 45th of 55). This is the largest performance gap in the suite. Strategic analysis measures nuanced tradeoff reasoning with real numbers — the kind of thinking needed for business analysis, competitive research, and executive decision support. Llama 4 Maverick's score of 2/5 here places it in the bottom tier of models tested.

• Faithfulness: Opus 4.7 scores 5/5 (tied for 1st among 56 models); Llama 4 Maverick scores 4/5 (ranked 35th of 56). Faithfulness measures how reliably a model sticks to source material without hallucinating. Opus 4.7 is at the ceiling; Llama 4 Maverick is above median but not elite.

• Long context: Opus 4.7 scores 5/5 (tied for 1st among 56 models); Llama 4 Maverick scores 4/5 (ranked 39th of 56). Both models share a roughly 1 million token context window, but Opus 4.7's retrieval accuracy at 30,000+ tokens is measurably better in our tests.

• Creative problem solving: Opus 4.7 scores 5/5 (tied for 1st among 55 models); Llama 4 Maverick scores 3/5 (ranked 31st of 55). This test rewards non-obvious, specific, feasible ideas. A 5 vs 3 suggests Opus 4.7 generates substantially more useful creative solutions.

• Constrained rewriting: Opus 4.7 scores 4/5 (ranked 6th of 55); Llama 4 Maverick scores 3/5 (ranked 32nd of 55). Compressing content within hard character limits while preserving meaning — Opus 4.7 handles this better.

• Safety calibration: Opus 4.7 scores 3/5 (ranked 10th of 56); Llama 4 Maverick scores 2/5 (ranked 13th of 56). Neither model is at the top here — the median across our 56-model set is 2/5, so both are at or slightly above average. Opus 4.7 has a small edge, ranking in the top 10.

Where they tie:

• Structured output: Both score 4/5 (both ranked 26th of 55). JSON schema compliance is equivalent between the two models.
• Classification: Both score 3/5 (both ranked 31st of 54). Accurate categorization and routing is a weak point for both.
• Persona consistency: Both score 5/5 (both tied for 1st among 55 models). Neither model has an edge in maintaining character or resisting prompt injection.
• Multilingual: Both score 4/5 (both ranked 36th of 56). Equivalent non-English output quality.

The pattern is clear: Llama 4 Maverick holds its own on structural tasks (formatting, consistency, language coverage) but falls well behind on reasoning-heavy evaluations — strategic thinking, planning, and creative problem solving.

The cost gap here is dramatic. Claude Opus 4.7 is priced at $5 per million input tokens and $25 per million output tokens. Llama 4 Maverick runs at $0.15 per million input tokens and $0.60 per million output tokens — making the output side approximately 42 times cheaper.

At 1 million output tokens per month, the difference is $25 versus $0.60 — essentially negligible in absolute terms, so the choice at that scale is purely about quality. At 10 million output tokens, that's $250 versus $6 per month. At 100 million output tokens — a realistic volume for production applications with significant traffic — you're looking at $2,500 per month for Claude Opus 4.7 versus just $60 for Llama 4 Maverick. That $2,440 monthly gap is hard to ignore.

For developers building consumer-facing apps with millions of requests, or for batch processing pipelines generating large volumes of text, Llama 4 Maverick's pricing is a serious structural advantage. For enterprise teams running lower-volume, high-stakes workflows — legal analysis, complex agentic systems, strategic research — the performance premium of Claude Opus 4.7 likely justifies the cost. The right question is not which is cheaper, but whether the quality delta is worth $2,400+ per 100 million tokens at your specific scale.

Choose Claude Opus 4.7 if:

• You are building or running agentic systems that require reliable multi-step planning and failure recovery — the 5 vs 3 gap on agentic planning in our tests is a material reliability difference.
• Your use case depends on strategic analysis or complex reasoning (business intelligence, legal review, research synthesis) — Llama 4 Maverick's 2/5 on strategic analysis places it near the bottom of tested models.
• Faithfulness to source documents is critical — hallucination risk is lower at Opus 4.7's 5/5 score.
• Output volume is under 10 million tokens per month, where the absolute cost difference remains manageable.
• You need a model with documented, tested tool calling performance (Llama 4 Maverick's test was rate-limited during our evaluation).

Choose Llama 4 Maverick if:

• You are running high-volume production workloads where $0.60 per million output tokens versus $25 per million is the deciding constraint — the savings reach $2,400+ per 100 million tokens.
• Your tasks skew toward structured output, classification, multilingual content, or persona-consistent interactions — the models are statistically equivalent on all four.
• You are prototyping or evaluating at scale and need to control API costs before optimizing for quality.
• The reasoning gap is acceptable for your use case — if strategic depth and agentic reliability are not requirements, you are paying a 42x premium for capabilities you are not using.