Model comparison
GLM-5.2 vs Ling 3.0 Flash
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.2 #40 (Estimated); Ling 3.0 Flash unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.2 and Ling 3.0 Flash share 0 comparable benchmark results. 0 of 8 categories are comparable. 42 results are unique to GLM-5.2; 0 to Ling 3.0 Flash.
Updated July 24, 2026- Shared results
- 0
- GLM-5.2 only
- 42
- Ling 3.0 Flash only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-5.2 and Ling 3.0 Flash is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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 does not have sourced benchmark coverage for Ling 3.0 Flash yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GLM-5.2 has the larger context window at 1M, compared with 262K for Ling 3.0 Flash.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.2 | Ling 3.0 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.2$1.4 input / $4.4 output | Ling 3.0 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-5.2Not available | Ling 3.0 FlashNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.2Not available | Ling 3.0 FlashNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.21M | Ling 3.0 Flash262K | GLM-5.2 lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-5.2 | Ling 3.0 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 81% | — | Not comparable |
| MCP AtlasSource | 76.8% | — | Not comparable |
| ToolathlonSource | 48.2% | — | Not comparable |
| AA Agentic IndexSource | 43.1% | — | Not comparable |
| τ²-bench resultsSource | 99.1% | — | Not comparable |
| GDPval-AASource | 50.5% | — | Not comparable |
| GDPval-AASource | 1510 | — | Not comparable |
| APEX-Agents-AASource | 33.7% | — | Not comparable |
| ResearchClawBenchSource | 20.7% | — | Not comparable |
| AA BriefcaseSource | 1254 | — | Not comparable |
| AA EnterpriseOps-GymSource | 42.7% | — | Not comparable |
| AA Harvey LABSource | 91.0% | — | Not comparable |
| AA ITBenchSource | 42.7% | — | Not comparable |
| AA Tau3 BankingSource | 26.8% | — | Not comparable |
| terminalBenchHardSource | 50.8% | — | Not comparable |
| aaTerminalBench21Source | 77.9% | — | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5.2 | Ling 3.0 Flash | Result |
|---|---|---|---|
| SWE-bench ProSource | 62.1% | — | Not comparable |
| NL2RepoSource | 48.9% | — | Not comparable |
| Terminal-Bench 2.0Source | 81.0% | — | Not comparable |
| ProgramBenchSource | 63.7% | — | Not comparable |
| cursorBench32Source | 55.0% | — | Not comparable |
| AA Coding IndexSource | 68.8% | — | Not comparable |
| AA-SciCodeSource | 50.5% | — | Not comparable |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | GLM-5.2 | Ling 3.0 Flash | Result |
|---|---|---|---|
| GPQASource | 91.2% | — | Not comparable |
| GPQA-DSource | 91.2% | — | Not comparable |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 40.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.1% | — | Not comparable |
| AA-GPQA DiamondSource | 89.5% | — | Not comparable |
| AA-HLESource | 40.1% | — | Not comparable |
| AA-Omniscience IndexSource | 4.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 28.1% | — | Not comparable |
| AA Openness IndexSource | 44.4% | — | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5.2 | Ling 3.0 Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1340 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-5.2 | Ling 3.0 Flash | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.3% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-5.2 and Ling 3.0 Flash 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-5.2 and Ling 3.0 Flash today?
GLM-5.2: $1.40 input / $4.40 output per 1M tokens Ling 3.0 Flash: Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.