Model comparison
Celeris-1 vs GLM-5
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-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and GLM-5 share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 48 to GLM-5.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- GLM-5 only
- 48
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if knowledge is the priority; GLM-5 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-5 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 $1.00 input / $3.20 output per 1M tokens for GLM-5. GLM-5 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-5 |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 9.5 | GLM-566.4 |
| Agentic | Celeris-1Not measured | MarginNo overlap | GLM-556.2 |
| Coding | Celeris-1Not measured | MarginNo overlap | GLM-566.3 |
| Reasoning | Celeris-1Not measured | MarginNo overlap | GLM-560.8 |
| Math | Celeris-1Not measured | MarginNo overlap | GLM-556.3 |
| Multilingual | Celeris-1Not measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | Celeris-1Not measured | MarginNo overlap | GLM-592.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
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- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 85.7%Winner: GLM-5Δ 9.8MMLU-Pro: Celeris-1 scored 75.9%; GLM-5 scored 85.7%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | GLM-5$1 input / $3.2 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | GLM-5200K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | Celeris-1 | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 56.2% | Not comparable |
| Claw-EvalSource | — | 57.7% | Not comparable |
| QwenClawBenchSource | — | 54.1% | Not comparable |
| τ³-bench resultsSource | — | 65.6% | Not comparable |
| DeepPlanningSource | — | 14.6% | Not comparable |
| ToolathlonSource | — | 38% | Not comparable |
| MCP AtlasSource | — | 31.1% | Not comparable |
| MCP-TasksSource | — | 60.8% | Not comparable |
| WideResearchSource | — | 69.8% | Not comparable |
| τ²-bench resultsSource | — | 98.2% | Not comparable |
| CyberGymSource | — | 43.2% | Not comparable |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
Coding7 benchmarks
| Benchmark | Celeris-1 | GLM-5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 77.8% | Not comparable |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE-bench ProSource | — | 55.1% | Not comparable |
| SWE MultilingualSource | — | 73.3% | Not comparable |
| SWE-RebenchSource | — | 62.8% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
| AA-SciCodeSource | — | 46.2% | Not comparable |
Reasoning4 benchmarks
KnowledgeCeleris-1 wins12 benchmarks
| Benchmark | Celeris-1 | GLM-5 | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 85.7% | GLM-5 leads |
| GPQASource | — | 86% | Not comparable |
| GPQA-DSource | — | 86.0% | Not comparable |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
| HLESource | — | 50.4% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 39.5% | Not comparable |
| AA-GPQA DiamondSource | — | 82.0% | Not comparable |
| AA-HLESource | — | 27.2% | Not comparable |
| AA-Omniscience IndexSource | — | 2.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.9% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.0% | Not comparable |
Math8 benchmarks
| Benchmark | Celeris-1 | GLM-5 | Result |
|---|---|---|---|
| AIME26Source | — | 95.8% | Not comparable |
| AIME25 (Arcee)Source | — | 93.3% | Not comparable |
| HMMT Feb 2025Source | — | 97.5% | Not comparable |
| HMMT Nov 2025Source | — | 96.9% | Not comparable |
| HMMT Feb 2026Source | — | 86.4% | Not comparable |
| MMAnswerBenchSource | — | 82.5% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 16.434% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 2.100% | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | Celeris-1 | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1278 | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or GLM-5?
Celeris-1 and GLM-5 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?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 66.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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