Model comparison
Claude Mythos 5 vs GPT-5.6 Luna
Head-to-head evidence from 6 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Mythos 5 #1 (Supported); GPT-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Mythos 5 and GPT-5.6 Luna share 6 comparable benchmark results. 5 of 8 categories are comparable. 9 results are unique to Claude Mythos 5; 35 to GPT-5.6 Luna.
Updated July 23, 2026- Shared results
- 6
- Claude Mythos 5 only
- 9
- GPT-5.6 Luna only
- 35
- Comparable categories
- 5 / 8
Pick Claude Mythos 5 if you want the stronger benchmark profile. GPT-5.6 Luna only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 3 evidence categories; 5 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Claude Mythos 5 is clearly ahead on the BenchAlign aggregate, 83.93 to 67.17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Mythos 5's sharpest advantage is in coding, where it averages 89.7 against 62.7. The single biggest benchmark swing on the page is SWE-bench Pro, 80.3% to 62.7%. GPT-5.6 Luna does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Claude Mythos 5 is also the more expensive model on tokens at $10.00 input / $50.00 output per 1M tokens, versus $1.00 input / $6.00 output per 1M tokens for GPT-5.6 Luna. That is roughly 8.3x on output cost alone. Claude Mythos 5 gives you the larger context window at 1M+, compared with 1M for GPT-5.6 Luna.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude Mythos 5 | Δ | GPT-5.6 Luna |
|---|---|---|---|
| Coding | Claude Mythos 589.7 | Margin← 27.0 | GPT-5.6 Luna62.7 |
| Math | Claude Mythos 597.6 | Margin← 24.0 | GPT-5.6 Luna73.6 |
| Knowledge | Claude Mythos 568.5 | Margin→ 23.8 | GPT-5.6 Luna92.3 |
| Multimodal | Claude Mythos 593.5 | Margin← 15.1 | GPT-5.6 Luna78.4 |
| Agentic | Claude Mythos 587.0 | Margin← 2.9 | GPT-5.6 Luna84.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 80.3%B 62.7%Winner: Claude Mythos 5Δ 17.6SWE-bench Pro: Claude Mythos 5 scored 80.3%; GPT-5.6 Luna scored 62.7%. Claude Mythos 5 wins this benchmark. - Source ↗
BrowseComp
AgenticA 88%B 83.3%Winner: Claude Mythos 5Δ 4.7BrowseComp: Claude Mythos 5 scored 88%; GPT-5.6 Luna scored 83.3%. Claude Mythos 5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 88%B 84.7%Winner: Claude Mythos 5Δ 3.3Terminal-Bench 2.0: Claude Mythos 5 scored 88%; GPT-5.6 Luna scored 84.7%. Claude Mythos 5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.1%B 92.3%Winner: Claude Mythos 5Δ 1.8GPQA: Claude Mythos 5 scored 94.1%; GPT-5.6 Luna scored 92.3%. Claude Mythos 5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Mythos 5 | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Mythos 5$10 input / $50 output | GPT-5.6 Luna$1 input / $6 output | GPT-5.6 Luna has the lower combined listed price. |
| Generation speedtokens per second | Claude Mythos 5Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Mythos 5Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Mythos 51M+ | GPT-5.6 Luna1M | Claude Mythos 5 lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Mythos 5 wins16 benchmarks
| Benchmark | Claude Mythos 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 88% | 84.7% | Claude Mythos 5 leads |
| OSWorld-VerifiedSource | 85% | — | Not comparable |
| BrowseCompSource | 88% | 83.3% | Claude Mythos 5 leads |
| ExploitGymSource | 17.5% | 12.4% | Claude Mythos 5 leads |
| OSWorld 2.0Source | — | 45.6% | Not comparable |
| CyberGymSource | — | 77.9% | Not comparable |
| ToolathlonSource | — | 53.4% | Not comparable |
| AA Agentic IndexSource | — | 45.6% | Not comparable |
| GDPval-AASource | — | 54.2% | Not comparable |
| GDPval-AASource | — | 1584 | Not comparable |
| AA Harvey LABSource | — | 87.9% | Not comparable |
| AA ITBenchSource | — | 40.3% | Not comparable |
| AA Tau3 BankingSource | — | 27.2% | Not comparable |
| AA AutomationBenchSource | — | 42.2% | Not comparable |
| aaTerminalBench21Source | — | 80.9% | Not comparable |
| APEX-Agents-AASource | — | 35.8% | Not comparable |
CodingClaude Mythos 5 wins8 benchmarks
| Benchmark | Claude Mythos 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 95.5% | — | Not comparable |
| SWE-bench ProSource | 80.3% | 62.7% | Claude Mythos 5 leads |
| Terminal-Bench 2.0Source | 88.0% | 84.7% | Claude Mythos 5 leads |
| deepSweSource | — | 67.2% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 55.1% | Not comparable |
| cursorBench32Source | — | 61.1% | Not comparable |
| AA Coding IndexSource | — | 71.5% | Not comparable |
| AA-SciCodeSource | — | 52.5% | Not comparable |
Reasoning3 benchmarks
KnowledgeGPT-5.6 Luna wins12 benchmarks
| Benchmark | Claude Mythos 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| GPQASource | 94.1% | 92.3% | Claude Mythos 5 leads |
| HLESource | 64.5% | — | Not comparable |
| HLE w/o toolsSource | 59% | — | Not comparable |
| GPQA-DSource | — | 92.3% | Not comparable |
| HealthBench ProfessionalSource | — | 55.7% | Not comparable |
| HealthBench HardSource | — | 32.0% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 51.2% | Not comparable |
| AA-GPQA DiamondSource | — | 91.1% | Not comparable |
| AA-HLESource | — | 37.2% | Not comparable |
| AA-Omniscience IndexSource | — | -11.2% | Not comparable |
| AA-Omniscience AccuracySource | — | 41.5% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 90.1% | Not comparable |
MathClaude Mythos 5 wins4 benchmarks
Multilingual1 benchmarks
| Benchmark | Claude Mythos 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE MultilingualSource | 92.2% | — | Not comparable |
MultimodalClaude Mythos 5 wins6 benchmarks
Frequently Asked Questions (6)
Which is better, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 is ahead on BenchLM's BenchAlign leaderboard, 83.93 to 67.17. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 80.3% and 62.7%.
Which is better for knowledge tasks, Claude Mythos 5 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for knowledge tasks in this comparison, averaging 92.3 versus 68.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 has the edge for coding in this comparison, averaging 89.7 versus 62.7. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 has the edge for math in this comparison, averaging 97.6 versus 73.6. GPT-5.6 Luna stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 has the edge for agentic tasks in this comparison, averaging 87 versus 84.1. Inside this category, ExploitGym is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 has the edge for multimodal and grounded tasks in this comparison, averaging 93.5 versus 78.4. GPT-5.6 Luna stays close enough that the answer can still flip depending on your workload.
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