Model comparison
GLM-5 vs Fugu Cyber
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: GLM-5 #28 (Supported); Fugu Cyber unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Fugu Cyber share 1 comparable benchmark result. 0 of 8 categories are comparable. 48 results are unique to GLM-5; 1 to Fugu Cyber.
Updated July 21, 2026- Shared results
- 1
- GLM-5 only
- 48
- Fugu Cyber only
- 1
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and Fugu Cyber is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Fugu Cyber is priced at $6.00 input / $36.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Fugu Cyber has the larger context window at 1M, compared with 200K for GLM-5.
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 | GLM-5 | Δ | Fugu Cyber |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | Fugu CyberNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Fugu CyberNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Fugu CyberNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Fugu CyberNot measured |
| Math | GLM-556.3 | MarginNo overlap | Fugu CyberNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Fugu CyberNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Fugu CyberNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Fugu Cyber | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Fugu Cyber$6 input / $36 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Fugu CyberNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Fugu CyberNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Fugu Cyber1M | Fugu Cyber lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | GLM-5 | Fugu Cyber | 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% | 86.9% | Fugu Cyber leads |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| CTI-REALMSource | — | 72.1% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | Fugu Cyber | 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
Knowledge12 benchmarks
| Benchmark | GLM-5 | Fugu Cyber | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | 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 | GLM-5 | Fugu Cyber | 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 | GLM-5 | Fugu Cyber | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-5 and Fugu Cyber on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-5 and Fugu Cyber today?
GLM-5: $1.00 input / $3.20 output per 1M tokens Fugu Cyber: $6.00 input / $36.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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