Model comparison
Claude Fable 5 vs GPT-5.6 Luna
Head-to-head evidence from 21 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Fable 5 #2 (Supported); GPT-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Fable 5 and GPT-5.6 Luna share 21 comparable benchmark results. 3 of 8 categories are comparable. 13 results are unique to Claude Fable 5; 20 to GPT-5.6 Luna.
Updated July 23, 2026- Shared results
- 21
- Claude Fable 5 only
- 13
- GPT-5.6 Luna only
- 20
- Comparable categories
- 3 / 8
Pick Claude Fable 5 if you want the stronger benchmark profile. GPT-5.6 Luna only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 21 shared benchmark results across 4 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Claude Fable 5 is clearly ahead on the BenchAlign aggregate, 83.68 to 67.17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Fable 5's sharpest advantage is in coding, where it averages 89.2 against 62.7. The single biggest benchmark swing on the page is SWE-bench Pro, 80% to 62.7%. GPT-5.6 Luna does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Claude Fable 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 Fable 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 Fable 5 | Δ | GPT-5.6 Luna |
|---|---|---|---|
| Coding | Claude Fable 589.2 | Margin← 26.5 | GPT-5.6 Luna62.7 |
| Multimodal | Claude Fable 557.9 | Margin→ 20.5 | GPT-5.6 Luna78.4 |
| Agentic | Claude Fable 584.6 | Margin← 0.5 | GPT-5.6 Luna84.1 |
| Knowledge | Claude Fable 5Not measured | MarginNo overlap | GPT-5.6 Luna92.3 |
| Math | Claude Fable 5Not measured | MarginNo overlap | GPT-5.6 Luna73.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 80%B 62.7%Winner: Claude Fable 5Δ 17.3SWE-bench Pro: Claude Fable 5 scored 80%; GPT-5.6 Luna scored 62.7%. Claude Fable 5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 84.3%B 84.7%Winner: GPT-5.6 LunaΔ 0.4Terminal-Bench 2.0: Claude Fable 5 scored 84.3%; GPT-5.6 Luna scored 84.7%. GPT-5.6 Luna wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Fable 5 | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Fable 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 Fable 5Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Fable 5Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Fable 51M+ | GPT-5.6 Luna1M | Claude Fable 5 lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Fable 5 wins20 benchmarks
| Benchmark | Claude Fable 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 84.3% | 84.7% | GPT-5.6 Luna leads |
| OSWorld-VerifiedSource | 85% | — | Not comparable |
| GDPval-AASource | 1748 | 1584 | Claude Fable 5 leads |
| AA Agentic IndexSource | 52.8% | 45.6% | Claude Fable 5 leads |
| τ²-bench resultsSource | 98.5% | — | Not comparable |
| GDPval-AASource | 62.4% | 54.2% | Claude Fable 5 leads |
| AA BriefcaseSource | 1574 | — | Not comparable |
| AA AutomationBenchSource | 48.6% | 42.2% | Claude Fable 5 leads |
| AA EnterpriseOps-GymSource | 51.1% | — | Not comparable |
| AA Harvey LABSource | 93.6% | 87.9% | Claude Fable 5 leads |
| AA Tau3 BankingSource | 26.8% | 27.2% | GPT-5.6 Luna leads |
| terminalBenchHardSource | 62.9% | — | Not comparable |
| aaTerminalBench21Source | 84.6% | 80.9% | Claude Fable 5 leads |
| BrowseCompSource | — | 83.3% | Not comparable |
| OSWorld 2.0Source | — | 45.6% | Not comparable |
| CyberGymSource | — | 77.9% | Not comparable |
| ExploitGymSource | — | 12.4% | Not comparable |
| ToolathlonSource | — | 53.4% | Not comparable |
| AA ITBenchSource | — | 40.3% | Not comparable |
| APEX-Agents-AASource | — | 35.8% | Not comparable |
CodingClaude Fable 5 wins11 benchmarks
| Benchmark | Claude Fable 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 95% | — | Not comparable |
| SWE-bench ProSource | 80% | 62.7% | Claude Fable 5 leads |
| FrontierCode 1.1 MainSource | 53.5% | — | Not comparable |
| Terminal-Bench 2.0Source | 84.3% | 84.7% | GPT-5.6 Luna leads |
| cursorBench31Source | 70.6% | — | Not comparable |
| cursorBench32Source | 70.5% | 61.1% | Claude Fable 5 leads |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 76.5% | 71.5% | Claude Fable 5 leads |
| AA-SciCodeSource | 60.2% | 52.5% | Claude Fable 5 leads |
| deepSweSource | — | 67.2% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 55.1% | Not comparable |
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Fable 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 59.9% | 51.2% | Claude Fable 5 leads |
| AA-GPQA DiamondSource | 92.6% | 91.1% | Claude Fable 5 leads |
| AA-HLESource | 53.3% | 37.2% | Claude Fable 5 leads |
| AA-Omniscience IndexSource | 40.2% | -11.2% | Claude Fable 5 leads |
| AA-Omniscience AccuracySource | 61.4% | 41.5% | Claude Fable 5 leads |
| AA-Omniscience Hallucination RateSource | 54.9% | 90.1% | Claude Fable 5 leads |
| GPQASource | — | 92.3% | Not comparable |
| GPQA-DSource | — | 92.3% | Not comparable |
| HealthBench ProfessionalSource | — | 55.7% | Not comparable |
| HealthBench HardSource | — | 32.0% | Not comparable |
Math3 benchmarks
MultimodalGPT-5.6 Luna wins6 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Fable 5 | GPT-5.6 Luna | Result |
|---|---|---|---|
| AA-IFBenchSource | 63.5% | — | Not comparable |
Frequently Asked Questions (4)
Which is better, Claude Fable 5 or GPT-5.6 Luna?
Claude Fable 5 is ahead on BenchLM's BenchAlign leaderboard, 83.68 to 67.17. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 80% and 62.7%.
Which is better for coding, Claude Fable 5 or GPT-5.6 Luna?
Claude Fable 5 has the edge for coding in this comparison, averaging 89.2 versus 62.7. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Fable 5 or GPT-5.6 Luna?
Claude Fable 5 has the edge for agentic tasks in this comparison, averaging 84.6 versus 84.1. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Claude Fable 5 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for multimodal and grounded tasks in this comparison, averaging 78.4 versus 57.9. Claude Fable 5 stays close enough that the answer can still flip depending on your workload.
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