Model comparison
GLM-5 vs Ling 2.6 Flash
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (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 and Ling 2.6 Flash share 12 comparable benchmark results. 3 of 8 categories are comparable. 37 results are unique to GLM-5; 6 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 12
- GLM-5 only
- 37
- Ling 2.6 Flash only
- 6
- Comparable categories
- 3 / 8
Pick GLM-5 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 3 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 is clearly ahead on the BenchAlign aggregate, 66.06 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 66.3 against 27. The single biggest benchmark swing on the page is GPQA, 86% to 59%.
Ling 2.6 Flash gives you the larger context window at 262K, compared with 200K for GLM-5.
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 | Ling 2.6 Flash |
|---|---|---|---|
| Coding | GLM-566.3 | Margin← 39.3 | Ling 2.6 Flash27.0 |
| Inst. Following | GLM-592.6 | Margin← 35.6 | Ling 2.6 Flash57.0 |
| Knowledge | GLM-566.4 | Margin← 7.4 | Ling 2.6 Flash59.0 |
| Agentic | GLM-556.2 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Math | GLM-556.3 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Ling 2.6 FlashNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 59%Winner: GLM-5Δ 27GPQA: GLM-5 scored 86%; Ling 2.6 Flash scored 59%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 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-51.64 s | Ling 2.6 Flash1.07 s | Ling 2.6 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | Ling 2.6 Flash262K | Ling 2.6 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-5 | Ling 2.6 Flash | 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% | 86% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| GDPval-AASource | — | 2.2% | Not comparable |
| GDPval-AASource | — | 545 | Not comparable |
| AA Agentic IndexSource | — | 2.3% | Not comparable |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | Ling 2.6 Flash | 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% | 27.1% | GLM-5 leads |
| SciCodeSource | — | 27% | Not comparable |
| AA Coding IndexSource | — | 25.3% | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins12 benchmarks
| Benchmark | GLM-5 | Ling 2.6 Flash | Result |
|---|---|---|---|
| GPQASource | 86% | 59% | GLM-5 leads |
| 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% | 14.1% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 59.3% | GLM-5 leads |
| AA-HLESource | 27.2% | 6.2% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -65.7% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 15.4% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 95.8% | GLM-5 leads |
Math8 benchmarks
| Benchmark | GLM-5 | Ling 2.6 Flash | 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 | Ling 2.6 Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (4)
Which is better, GLM-5 or Ling 2.6 Flash?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 86% and 59%.
Which is better for knowledge tasks, GLM-5 or Ling 2.6 Flash?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 59. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or Ling 2.6 Flash?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 27. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or Ling 2.6 Flash?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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