Model comparison
Celeris-1 vs GLM-4.7
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Celeris-1 unranked (Not scored); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and GLM-4.7 share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 29 to GLM-4.7.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- GLM-4.7 only
- 29
- 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-4.7 is the better fit if you want the cheaper token bill or you need the larger 200K context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 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-4.7 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 $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 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-4.7 gives you the larger context window at 200K, compared with 8K for Celeris-1.
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 | Celeris-1 | Δ | GLM-4.7 |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 24.1 | GLM-4.751.8 |
| Agentic | Celeris-1Not measured | MarginNo overlap | GLM-4.745.7 |
| Coding | Celeris-1Not measured | MarginNo overlap | GLM-4.775.4 |
| Math | Celeris-1Not measured | MarginNo overlap | GLM-4.71.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 84.3%Winner: GLM-4.7Δ 8.4MMLU-Pro: Celeris-1 scored 75.9%; GLM-4.7 scored 84.3%. GLM-4.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | GLM-4.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | GLM-4.7$0 input / $0 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | GLM-4.782 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | GLM-4.71.10 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | GLM-4.7200K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | Celeris-1 | GLM-4.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 41% | Not comparable |
| BrowseCompSource | — | 52% | Not comparable |
| VITA-BenchSource | — | 15.5% | Not comparable |
| AA Agentic IndexSource | — | 25.4% | Not comparable |
| τ²-bench resultsSource | — | 95.9% | Not comparable |
| Gert LabsSource | — | 39.95% | Not comparable |
| GDPval-AASource | — | 33.3% | Not comparable |
| GDPval-AASource | — | 1165 | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
KnowledgeCeleris-1 wins9 benchmarks
| Benchmark | Celeris-1 | GLM-4.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 84.3% | GLM-4.7 leads |
| GPQASource | — | 85.7% | Not comparable |
| HLESource | — | 24.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.7% | Not comparable |
| AA-GPQA DiamondSource | — | 85.9% | Not comparable |
| AA-HLESource | — | 25.1% | Not comparable |
| AA-Omniscience IndexSource | — | -34.6% | Not comparable |
| AA-Omniscience AccuracySource | — | 29.3% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 90.3% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | Celeris-1 | GLM-4.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1255 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | GLM-4.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.9% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or GLM-4.7?
Celeris-1 and GLM-4.7 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-4.7?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 51.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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