Model comparison
GLM-4.7 vs Ling 2.6 Flash
Head-to-head evidence from 16 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Ling 2.6 Flash share 16 comparable benchmark results. 2 of 8 categories are comparable. 14 results are unique to GLM-4.7; 2 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 16
- GLM-4.7 only
- 14
- Ling 2.6 Flash only
- 2
- Comparable categories
- 2 / 8
Pick GLM-4.7 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 16 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-4.7 is clearly ahead on the BenchAlign aggregate, 61.16 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 27. The single biggest benchmark swing on the page is GPQA, 85.7% to 59%. 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-4.7 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 200K for GLM-4.7.
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-4.7 | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Coding | GLM-4.775.4 | Margin← 48.4 | Ling 2.6 Flash27.0 |
| Knowledge | GLM-4.751.8 | Margin→ 7.2 | Ling 2.6 Flash59.0 |
| Agentic | GLM-4.745.7 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | Ling 2.6 Flash57.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 85.7%B 59%Winner: GLM-4.7Δ 26.7GPQA: GLM-4.7 scored 85.7%; Ling 2.6 Flash scored 59%. GLM-4.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | Ling 2.6 Flash209.5 tok/s | Ling 2.6 Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Ling 2.6 Flash1.07 s | Ling 2.6 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | Ling 2.6 Flash262K | Ling 2.6 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | Ling 2.6 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 2.3% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 86% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 2.2% | GLM-4.7 leads |
| GDPval-AASource | 1165 | 545 | GLM-4.7 leads |
CodingGLM-4.7 wins7 benchmarks
| Benchmark | GLM-4.7 | Ling 2.6 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 25.3% | GLM-4.7 leads |
| AA-SciCodeSource | 45.1% | 27.1% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SciCodeSource | — | 27% | Not comparable |
Reasoning2 benchmarks
KnowledgeLing 2.6 Flash wins9 benchmarks
| Benchmark | GLM-4.7 | Ling 2.6 Flash | Result |
|---|---|---|---|
| GPQASource | 85.7% | 59% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 14.1% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 59.3% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 6.2% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -65.7% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 15.4% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 95.8% | GLM-4.7 leads |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Ling 2.6 Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-4.7 or Ling 2.6 Flash?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 59%.
Which is better for knowledge tasks, GLM-4.7 or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 51.8. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or Ling 2.6 Flash?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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