Model comparison
Celeris-1 vs GPT-5.6 Luna
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Celeris-1 unranked (Not scored); GPT-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and GPT-5.6 Luna share 0 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Celeris-1; 42 to GPT-5.6 Luna.
Updated July 24, 2026- Shared results
- 0
- Celeris-1 only
- 1
- GPT-5.6 Luna only
- 42
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; GPT-5.6 Luna is the better fit if knowledge is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Celeris-1 and GPT-5.6 Luna finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GPT-5.6 Luna is the reasoning model in the pair, while Celeris-1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.6 Luna gives you the larger context window at 1M, compared with 8K for Celeris-1.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | GPT-5.6 Luna$1 input / $6 output | GPT-5.6 Luna has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | GPT-5.6 Luna1M | GPT-5.6 Luna lists the larger context window. |
Benchmark Deep Dive
Agentic15 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 84.7% | Not comparable |
| 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 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 |
Coding7 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE-bench ProSource | — | 62.7% | Not comparable |
| Terminal-Bench 2.0Source | — | 84.7% | Not comparable |
| 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 |
Reasoning4 benchmarks
KnowledgeGPT-5.6 Luna wins11 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | — | Not comparable |
| GPQASource | — | 92.3% | 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 |
Math3 benchmarks
Frequently Asked Questions (2)
Which is better, Celeris-1 or GPT-5.6 Luna?
Celeris-1 and GPT-5.6 Luna are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Celeris-1 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for knowledge tasks in this comparison, averaging 92.3 versus 75.9. Celeris-1 stays close enough that the answer can still flip depending on your workload.
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