Llama 4 Maverick vs Llama 4 Scout
Empirical evaluation across autonomous coding benchmarks (SWE-bench Verified, LiveCodeBench), token economics, and multi-agent topology in Swarm.
For autonomous agentic coding where multi-file patch pass rate is paramount, Llama 4 Scout holds the verified advantage (-7.8% difference). Llama 4 Scout is 1.33x cheaper on input tokens ($0.6/M vs $0.8/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Next-Gen Open Frontier Engine). Excellent peer redundancy or parallel Coder swarm execution.
Llama 4 Maverick
Coder SubagentLlama 4 Scout
Finder SubagentStandardized Coding & Reasoning Benchmarks
Verified, contamination-free benchmark scores with direct mathematical deltas.
| Benchmark Metric | Llama 4 Maverick | Llama 4 Scout | Performance Delta |
|---|---|---|---|
| SWE-bench VerifiedReal-world GitHub issues resolved autonomously | 61.2% | 69% | Llama 4 Scout (-7.8% difference) |
| LiveCodeBenchPost-training cutoff algorithmic challenges | 55.4% | 61% | Llama 4 Scout (-5.6% difference) |
| LMSYS Coding EloCrowdsourced blind developer preference | 1370 | 1380 | Llama 4 Scout (-10Elo difference) |
| AIME (Competition Math)Deep multi-step deductive reasoning | 78.5% | — | Llama 4 Maverick (Only measured on 78.5) |
Token Economics & Operational Cost Ratio
Pricing per 1M tokens and prompt caching advantages for long-horizon sessions.
Llama 4 Scout is 1.33x cheaper on input tokens ($0.6/M vs $0.8/M).
Both models share identical completion pricing.
Prompt caching significantly reduces long-horizon AST parsing costs during multi-turn Swarm pairing sessions.
Swarm Multi-Agent Topology Synergy
How Swarm orchestrates Llama 4 Maverick and Llama 4 Scout in autonomous engineering clusters.
Llama 4 Maverick
Recommended: Coder Subagent
High-fidelity code generation and vision capabilities make it ideal for full-stack web and systems coding.
Llama 4 Scout
Recommended: Finder Subagent
Ingests entire monorepos to produce comprehensive Workspace Maps.
When to Choose Which Model
Practical developer recommendations based on real-world engineering workloads.
Choose Llama 4 Maverick if:
- ✓61.2% on SWE-bench Verified
- ✓Strong native vision capabilities for web UI generation and wireframe translation
- ✓Fast inference on modern tensor core architectures
- ✓Need Coder Subagent specialization inside Swarm.
Choose Llama 4 Scout if:
- ✓Unrivaled 10M token context capacity with perfect needle-in-haystack retrieval
- ✓Strong 69.0% SWE-bench Verified score across cross-repository dependencies
- ✓Open-weights availability across major cloud platforms
- ✓Need Finder Subagent specialization inside Swarm.
Frequently Asked Comparison Questions
Which model is better for autonomous software engineering: Llama 4 Maverick or Llama 4 Scout?
For autonomous agentic coding where multi-file patch pass rate is paramount, Llama 4 Scout holds the verified advantage (-7.8% difference). Llama 4 Scout is 1.33x cheaper on input tokens ($0.6/M vs $0.8/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Next-Gen Open Frontier Engine). Excellent peer redundancy or parallel Coder swarm execution. On SWE-bench Verified, Llama 4 Maverick scores 61.2% vs Llama 4 Scout at 69%.
How do Llama 4 Maverick and Llama 4 Scout compare on token pricing and operational economics?
Llama 4 Scout is 1.33x cheaper on input tokens ($0.6/M vs $0.8/M). On cached prompts, Llama 4 Maverick costs $0.2/M vs Llama 4 Scout at $0.1/M.
How does Swarm configure Llama 4 Maverick and Llama 4 Scout in multi-agent workflows?
Both models sit in Tier 2 (Tier 2: Next-Gen Open Frontier Engine). Excellent peer redundancy or parallel Coder swarm execution. Swarm isolates code modifications in independent Git worktrees, allowing both models to operate in parallel without colliding working directories or git branch conflicts.