Model comparison
GLM-5.1 vs Ling 2.6 Flash
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and Ling 2.6 Flash share 15 comparable benchmark results. 2 of 8 categories are comparable. 21 results are unique to GLM-5.1; 3 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 15
- GLM-5.1 only
- 21
- Ling 2.6 Flash only
- 3
- Comparable categories
- 2 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 5 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.1 is clearly ahead on the BenchAlign aggregate, 67.74 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in coding, where it averages 61.3 against 27. Ling 2.6 Flash does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is the reasoning model in the pair, while Ling 2.6 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Ling 2.6 Flash gives you the larger context window at 262K, compared with 203K for GLM-5.1.
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 | GLM-5.1 | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Coding | GLM-5.161.3 | Margin← 34.3 | Ling 2.6 Flash27.0 |
| Knowledge | GLM-5.152.3 | Margin→ 6.7 | Ling 2.6 Flash59.0 |
| Agentic | GLM-5.165.4 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Math | GLM-5.162.0 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Inst. Following | GLM-5.1Not measured | MarginNo overlap | Ling 2.6 Flash57.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-5.1Not available | Ling 2.6 Flash209.5 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Ling 2.6 Flash1.07 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Ling 2.6 Flash262K | Ling 2.6 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | GLM-5.1 | Ling 2.6 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | — | Not comparable |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | 2.3% | GLM-5.1 leads |
| τ²-bench resultsSource | 97.7% | 86% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | 2.2% | GLM-5.1 leads |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | 545 | GLM-5.1 leads |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
CodingGLM-5.1 wins7 benchmarks
| Benchmark | GLM-5.1 | Ling 2.6 Flash | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | — | Not comparable |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | — | Not comparable |
| AA Coding IndexSource | 55.8% | 25.3% | GLM-5.1 leads |
| AA-SciCodeSource | 43.8% | 27.1% | GLM-5.1 leads |
| SciCodeSource | — | 27% | Not comparable |
Reasoning2 benchmarks
KnowledgeLing 2.6 Flash wins9 benchmarks
| Benchmark | GLM-5.1 | Ling 2.6 Flash | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | — | Not comparable |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | 14.1% | GLM-5.1 leads |
| AA-GPQA DiamondSource | 86.8% | 59.3% | GLM-5.1 leads |
| AA-HLESource | 28.0% | 6.2% | GLM-5.1 leads |
| AA-Omniscience IndexSource | 1.9% | -65.7% | GLM-5.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 15.4% | GLM-5.1 leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 95.8% | GLM-5.1 leads |
| GPQASource | — | 59% | Not comparable |
Math6 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5.1 | Ling 2.6 Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5.1 or Ling 2.6 Flash?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 43.87.
Which is better for knowledge tasks, GLM-5.1 or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 52.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or Ling 2.6 Flash?
GLM-5.1 has the edge for coding in this comparison, averaging 61.3 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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