OpenAI GPT-6 Luna vs OpenAI o4-mini
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, OpenAI GPT-6 Luna holds the verified advantage (+7.7% advantage). OpenAI GPT-6 Luna is 10x cheaper on input tokens ($0.12/M vs $1.2/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Extreme Efficiency Subagent). Excellent peer redundancy or parallel Coder swarm execution.
OpenAI GPT-6 Luna
Coder SubagentOpenAI o4-mini
Coder SubagentStandardized Coding & Reasoning Benchmarks
Verified, contamination-free benchmark scores with direct mathematical deltas.
| Benchmark Metric | OpenAI GPT-6 Luna | OpenAI o4-mini | Performance Delta |
|---|---|---|---|
| SWE-bench VerifiedReal-world GitHub issues resolved autonomously | 64.5% | 56.8% | OpenAI GPT-6 Luna (+7.7% advantage) |
| LiveCodeBenchPost-training cutoff algorithmic challenges | 56.8% | 73.4% | OpenAI o4-mini (-16.6% difference) |
| LMSYS Coding EloCrowdsourced blind developer preference | 1358 | 1385 | OpenAI o4-mini (-27Elo difference) |
| AIME (Competition Math)Deep multi-step deductive reasoning | — | 90.1% | OpenAI o4-mini (Only measured on 90.1) |
Token Economics & Operational Cost Ratio
Pricing per 1M tokens and prompt caching advantages for long-horizon sessions.
OpenAI GPT-6 Luna is 10x cheaper on input tokens ($0.12/M vs $1.2/M).
Output generation costs differ by 10x.
Prompt caching significantly reduces long-horizon AST parsing costs during multi-turn Swarm pairing sessions.
Swarm Multi-Agent Topology Synergy
How Swarm orchestrates OpenAI GPT-6 Luna and OpenAI o4-mini in autonomous engineering clusters.
OpenAI GPT-6 Luna
Recommended: Coder Subagent
Handles dozens of parallel subtasks at near-zero incremental cost.
OpenAI o4-mini
Recommended: Coder Subagent
Combines reasoning depth with rapid execution, making it the premier subagent for complex algorithmic implementation jobs in Task Programs.
When to Choose Which Model
Practical developer recommendations based on real-world engineering workloads.
Choose OpenAI GPT-6 Luna if:
- ✓Absolute lowest cost per task ($0.07) in the Artificial Analysis Index
- ✓High-throughput 157 TPS streaming rate
- ✓Reliable OpenAI tool use and schema validation
- ✓Need Coder Subagent specialization inside Swarm.
Choose OpenAI o4-mini if:
- ✓56.8% on SWE-bench Verified at lightweight cost ($1.20/M input)
- ✓Ultra-fast reasoning output
- ✓Refined JSON schema adherence and structured tool calling
- ✓Need Coder Subagent specialization inside Swarm.
Frequently Asked Comparison Questions
Which model is better for autonomous software engineering: OpenAI GPT-6 Luna or OpenAI o4-mini?
For autonomous agentic coding where multi-file patch pass rate is paramount, OpenAI GPT-6 Luna holds the verified advantage (+7.7% advantage). OpenAI GPT-6 Luna is 10x cheaper on input tokens ($0.12/M vs $1.2/M). In Swarm multi-agent clusters, Both models sit in Tier 2 (Tier 2: Extreme Efficiency Subagent). Excellent peer redundancy or parallel Coder swarm execution. On SWE-bench Verified, OpenAI GPT-6 Luna scores 64.5% vs OpenAI o4-mini at 56.8%.
How do OpenAI GPT-6 Luna and OpenAI o4-mini compare on token pricing and operational economics?
OpenAI GPT-6 Luna is 10x cheaper on input tokens ($0.12/M vs $1.2/M). On cached prompts, OpenAI GPT-6 Luna costs $0.02/M vs OpenAI o4-mini at $0.4/M.
How does Swarm configure OpenAI GPT-6 Luna and OpenAI o4-mini in multi-agent workflows?
Both models sit in Tier 2 (Tier 2: Extreme Efficiency Subagent). 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.