Model comparison
GLM-5 vs GLM-5 (Reasoning)
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Sibling matchup inside the GLM-5 family.
Public leaderboard positions: GLM-5 #28 (Supported); GLM-5 (Reasoning) #52 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and GLM-5 (Reasoning) share 1 comparable benchmark result. 0 of 8 categories are comparable. 48 results are unique to GLM-5; 1 to GLM-5 (Reasoning).
Updated July 20, 2026- Shared results
- 1
- GLM-5 only
- 48
- GLM-5 (Reasoning) only
- 1
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and GLM-5 (Reasoning) 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.
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 | GLM-5 (Reasoning) |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Coding | GLM-566.3 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Reasoning | GLM-560.8 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Knowledge | GLM-566.4 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Math | GLM-556.3 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | GLM-5 (Reasoning)Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | GLM-5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | GLM-5 (Reasoning)$1 input / $3.2 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-574 tok/s | GLM-5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | GLM-5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | GLM-5 (Reasoning)200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | GLM-5 (Reasoning) | 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 |
Coding8 benchmarks
| Benchmark | GLM-5 | GLM-5 (Reasoning) | 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 |
| Vibe Code BenchSource | — | 23.36% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | GLM-5 (Reasoning) | 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 | GLM-5 (Reasoning) | 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 | GLM-5 (Reasoning) | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1280 | 1280 | Tie |
Frequently Asked Questions (3)
Can I compare GLM-5 and GLM-5 (Reasoning) 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 GLM-5 (Reasoning) today?
GLM-5: $1.00 input / $3.20 output per 1M tokens GLM-5 (Reasoning): $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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