Model comparison
Celeris-1 vs GPT-5.6 Sol
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 Sol #3 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and GPT-5.6 Sol share 0 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Celeris-1; 47 to GPT-5.6 Sol.
Updated July 24, 2026- Shared results
- 0
- Celeris-1 only
- 1
- GPT-5.6 Sol only
- 47
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model; GPT-5.6 Sol 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 Sol 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 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $2.00 input / $6.00 output per 1M tokens for Celeris-1. That is roughly 5.0x on output cost alone. GPT-5.6 Sol 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 Sol 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 Sol | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | GPT-5.6 Sol$5 input / $30 output | Celeris-1 has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | GPT-5.6 SolNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | GPT-5.6 SolNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | GPT-5.6 Sol1M | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Sol | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 91.9% | Not comparable |
| BrowseCompSource | — | 92.2% | Not comparable |
| OSWorld 2.0Source | — | 62.6% | Not comparable |
| CyberGymSource | — | 84.5% | Not comparable |
| ExploitGymSource | — | 33.7% | Not comparable |
| ToolathlonSource | — | 58% | Not comparable |
| AA Agentic IndexSource | — | 54.0% | Not comparable |
| τ²-bench resultsSource | — | 85.1% | Not comparable |
| GDPval-AASource | — | 61.8% | Not comparable |
| GDPval-AASource | — | 1736 | Not comparable |
| AA BriefcaseSource | — | 1501 | Not comparable |
| AA ITBenchSource | — | 56.2% | Not comparable |
| AA Tau3 BankingSource | — | 33.0% | Not comparable |
| AA AutomationBenchSource | — | 51.2% | Not comparable |
| AA Harvey LABSource | — | 87.2% | Not comparable |
| terminalBenchHardSource | — | 65.9% | Not comparable |
| aaTerminalBench21Source | — | 88% | Not comparable |
Coding8 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Sol | Result |
|---|---|---|---|
| SWE-bench ProSource | — | 64.6% | Not comparable |
| Terminal-Bench 2.0Source | — | 91.9% | Not comparable |
| deepSweSource | — | 72.7% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 60.6% | Not comparable |
| cursorBench32Source | — | 67.2% | Not comparable |
| VulcanBench v3Source | — | 87.0% | Not comparable |
| AA Coding IndexSource | — | 77.4% | Not comparable |
| AA-SciCodeSource | — | 56.1% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.6 Sol wins11 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Sol | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | — | Not comparable |
| GPQASource | — | 94.6% | Not comparable |
| GPQA-DSource | — | 94.6% | Not comparable |
| HealthBench ProfessionalSource | — | 60.5% | Not comparable |
| HealthBench HardSource | — | 33.1% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 58.9% | Not comparable |
| AA-GPQA DiamondSource | — | 94.1% | Not comparable |
| AA-HLESource | — | 47.2% | Not comparable |
| AA-Omniscience IndexSource | — | 21.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 58.5% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 88.8% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 72.7% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or GPT-5.6 Sol?
Celeris-1 and GPT-5.6 Sol 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 Sol?
GPT-5.6 Sol has the edge for knowledge tasks in this comparison, averaging 94.6 versus 75.9. Celeris-1 stays close enough that the answer can still flip depending on your workload.
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