Model comparison
GLM-4.5-Air vs GLM-5
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.5-Air #133 (Supported); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.5-Air and GLM-5 share 12 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to GLM-4.5-Air; 37 to GLM-5.
Updated July 22, 2026- Shared results
- 12
- GLM-4.5-Air only
- 0
- GLM-5 only
- 37
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.5-Air and GLM-5 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 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.
GLM-5 is priced at $1.00 input / $3.20 output per 1M tokens, versus $0.20 input / $1.10 output per 1M tokens for GLM-4.5-Air. GLM-5 has the larger context window at 200K, compared with 128K for GLM-4.5-Air.
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-4.5-Air | Δ | GLM-5 |
|---|---|---|---|
| Agentic | GLM-4.5-AirNot measured | MarginNo overlap | GLM-556.2 |
| Coding | GLM-4.5-AirNot measured | MarginNo overlap | GLM-566.3 |
| Reasoning | GLM-4.5-AirNot measured | MarginNo overlap | GLM-560.8 |
| Knowledge | GLM-4.5-AirNot measured | MarginNo overlap | GLM-566.4 |
| Math | GLM-4.5-AirNot measured | MarginNo overlap | GLM-556.3 |
| Multilingual | GLM-4.5-AirNot measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | GLM-4.5-AirNot measured | MarginNo overlap | GLM-592.6 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.5-Air | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.5-Air$0.2 input / $1.1 output | GLM-5$1 input / $3.2 output | GLM-4.5-Air has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.5-Air106 tok/s | GLM-574 tok/s | GLM-4.5-Air has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.5-Air1.18 s | GLM-51.64 s | GLM-4.5-Air reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.5-Air128K | GLM-5200K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-4.5-Air | GLM-5 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 46.5% | 98.2% | GLM-5 leads |
| 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 |
| CyberGymSource | — | 43.2% | Not comparable |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-4.5-Air | GLM-5 | Result |
|---|---|---|---|
| AA-SciCodeSource | 30.6% | 46.2% | GLM-5 leads |
| 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 |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-4.5-Air | GLM-5 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 16.5% | 39.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 73.3% | 82.0% | GLM-5 leads |
| AA-HLESource | 6.8% | 27.2% | GLM-5 leads |
| AA-Omniscience IndexSource | -62.5% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 15.5% | 26.9% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 92.3% | 34.0% | GLM-5 leads |
| 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 |
Math8 benchmarks
| Benchmark | GLM-4.5-Air | 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 | GLM-4.5-Air | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1176 | 1278 | GLM-5 leads |
Frequently Asked Questions (3)
Can I compare GLM-4.5-Air and GLM-5 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-4.5-Air and GLM-5 today?
GLM-4.5-Air: $0.20 input / $1.10 output per 1M tokens GLM-5: $1.00 input / $3.20 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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