Model comparison
Celeris-1 vs GLM-5.2
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); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and GLM-5.2 share 0 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Celeris-1; 43 to GLM-5.2.
Updated July 24, 2026- Shared results
- 0
- Celeris-1 only
- 1
- GLM-5.2 only
- 43
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; GLM-5.2 is the better fit if you want the cheaper token bill 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 GLM-5.2 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.
Celeris-1 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. GLM-5.2 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. GLM-5.2 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 | GLM-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | GLM-5.2$1.4 input / $4.4 output | GLM-5.2 has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | GLM-5.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | GLM-5.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | GLM-5.21M | GLM-5.2 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | Celeris-1 | GLM-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 81% | Not comparable |
| MCP AtlasSource | — | 76.8% | Not comparable |
| ToolathlonSource | — | 48.2% | Not comparable |
| AA Agentic IndexSource | — | 43.1% | Not comparable |
| τ²-bench resultsSource | — | 99.1% | Not comparable |
| GDPval-AASource | — | 50.7% | Not comparable |
| GDPval-AASource | — | 1514 | Not comparable |
| APEX-Agents-AASource | — | 33.7% | Not comparable |
| ResearchClawBenchSource | — | 20.7% | Not comparable |
| AA BriefcaseSource | — | 1260 | Not comparable |
| AA AutomationBenchSource | — | 27.8% | Not comparable |
| AA EnterpriseOps-GymSource | — | 42.7% | Not comparable |
| AA Harvey LABSource | — | 91.0% | Not comparable |
| AA ITBenchSource | — | 42.7% | Not comparable |
| AA Tau3 BankingSource | — | 26.8% | Not comparable |
| terminalBenchHardSource | — | 50.8% | Not comparable |
| aaTerminalBench21Source | — | 77.9% | Not comparable |
Coding7 benchmarks
| Benchmark | Celeris-1 | GLM-5.2 | Result |
|---|---|---|---|
| SWE-bench ProSource | — | 62.1% | Not comparable |
| NL2RepoSource | — | 48.9% | Not comparable |
| Terminal-Bench 2.0Source | — | 81.0% | Not comparable |
| ProgramBenchSource | — | 63.7% | Not comparable |
| cursorBench32Source | — | 55.0% | Not comparable |
| AA Coding IndexSource | — | 68.8% | Not comparable |
| AA-SciCodeSource | — | 50.5% | Not comparable |
Reasoning2 benchmarks
KnowledgeCeleris-1 wins12 benchmarks
| Benchmark | Celeris-1 | GLM-5.2 | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | — | Not comparable |
| GPQASource | — | 91.2% | Not comparable |
| GPQA-DSource | — | 91.2% | Not comparable |
| HLESource | — | 54.7% | Not comparable |
| HLE w/o toolsSource | — | 40.5% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 51.1% | Not comparable |
| AA-GPQA DiamondSource | — | 89.5% | Not comparable |
| AA-HLESource | — | 40.1% | Not comparable |
| AA-Omniscience IndexSource | — | 4.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 25.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 28.1% | Not comparable |
| AA Openness IndexSource | — | 44.4% | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | Celeris-1 | GLM-5.2 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1340 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | GLM-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 73.3% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or GLM-5.2?
Celeris-1 and GLM-5.2 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 GLM-5.2?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 59.6. GLM-5.2 stays close enough that the answer can still flip depending on your workload.
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