Model comparison
GLM-5.2 vs Inkling
Head-to-head evidence from 9 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-5.2 #5; Inkling #17
BenchAlign evidence: GLM-5.2 estimated; Inkling not scored. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.2 and Inkling share 9 comparable benchmark results. 4 of 8 categories are comparable. 34 results are unique to GLM-5.2; 7 to Inkling.
Updated July 15, 2026- Shared results
- 9
- GLM-5.2 only
- 34
- Inkling only
- 7
- Comparable categories
- 4 / 8
Pick GLM-5.2 if you want the stronger benchmark profile. Inkling only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 9 shared benchmark results across 4 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.2 is clearly ahead on the provisional aggregate, 81 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in agentic, where it averages 81 against 69.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 63.8%. Inkling does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Inkling is also the more expensive model on tokens at $1.87 input / $4.68 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. GLM-5.2 is the reasoning model in the pair, while Inkling 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.
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-5.2 | Δ | Inkling |
|---|---|---|---|
| Agentic | GLM-5.281.0 | Margin← 11.6 | Inkling69.4 |
| Knowledge | GLM-5.259.6 | Margin← 7.9 | Inkling51.7 |
| Coding | GLM-5.262.1 | Margin→ 6.5 | Inkling68.6 |
| Math | GLM-5.295.9 | Margin→ 1.2 | Inkling97.1 |
| Multimodal | GLM-5.2Not measured | MarginNo overlap | Inkling76.5 |
| Inst. Following | GLM-5.2Not measured | MarginNo overlap | Inkling79.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 81%B 63.8%Winner: GLM-5.2Δ 17.2Terminal-Bench 2.0: GLM-5.2 scored 81%; Inkling scored 63.8%. GLM-5.2 wins this benchmark. - Source ↗
HLE
KnowledgeA 54.7%B 46%Winner: GLM-5.2Δ 8.7HLE: GLM-5.2 scored 54.7%; Inkling scored 46%. GLM-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 62.1%B 54.3%Winner: GLM-5.2Δ 7.8SWE-bench Pro: GLM-5.2 scored 62.1%; Inkling scored 54.3%. GLM-5.2 wins this benchmark. - Source ↗
GPQA
KnowledgeA 91.2%B 87.9%Winner: GLM-5.2Δ 3.3GPQA: GLM-5.2 scored 91.2%; Inkling scored 87.9%. GLM-5.2 wins this benchmark. - Source ↗
AIME26
MathA 99.2%B 97.1%Winner: GLM-5.2Δ 2.1AIME26: GLM-5.2 scored 99.2%; Inkling scored 97.1%. GLM-5.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.2 | Inkling | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.2$1.4 input / $4.4 output | Inkling$1.87 input / $4.68 output | GLM-5.2 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.2Not available | InklingNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.2Not available | InklingNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.21M | Inkling1M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGLM-5.2 wins17 benchmarks
| Benchmark | GLM-5.2 | Inkling | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 81% | 63.8% | GLM-5.2 leads |
| MCP AtlasSource | 76.8% | 74.1% | GLM-5.2 leads |
| ToolathlonSource | 48.2% | — | Not comparable |
| AA Agentic IndexSource | 43.1% | — | Not comparable |
| τ²-bench resultsSource | 99.1% | — | Not comparable |
| GDPval-AASource | 50.7% | — | Not comparable |
| GDPval-AASource | 1514 | — | Not comparable |
| APEX-Agents-AASource | 33.7% | — | Not comparable |
| ResearchClawBenchSource | 20.7% | — | Not comparable |
| AA BriefcaseSource | 1260 | — | Not comparable |
| AA AutomationBenchSource | 27.8% | — | Not comparable |
| AA EnterpriseOps-GymSource | 42.7% | — | Not comparable |
| AA Harvey LABSource | 7.5% | — | Not comparable |
| AA ITBenchSource | 42.7% | — | Not comparable |
| AA Tau3 BankingSource | 26.8% | — | Not comparable |
| BrowseCompSource | — | 77.1% | Not comparable |
| Design Arena Agentic Web DevSource | — | 1258 | Not comparable |
CodingInkling wins10 benchmarks
| Benchmark | GLM-5.2 | Inkling | Result |
|---|---|---|---|
| SWE-bench ProSource | 62.1% | 54.3% | GLM-5.2 leads |
| NL2RepoSource | 48.9% | — | Not comparable |
| Terminal-Bench 2.0Source | 81.0% | 63.8% | GLM-5.2 leads |
| ProgramBenchSource | 63.7% | — | Not comparable |
| cursorBench32Source | 55.0% | — | Not comparable |
| AA Coding IndexSource | 68.8% | — | Not comparable |
| Terminal-Bench HardSource | 50.8% | — | Not comparable |
| AA-SciCodeSource | 50.5% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 77.9% | — | Not comparable |
| SWE-bench VerifiedSource | — | 77.6% | Not comparable |
Reasoning2 benchmarks
KnowledgeGLM-5.2 wins11 benchmarks
| Benchmark | GLM-5.2 | Inkling | Result |
|---|---|---|---|
| GPQASource | 91.2% | 87.9% | GLM-5.2 leads |
| GPQA-DSource | 91.2% | 87.9% | GLM-5.2 leads |
| HLESource | 54.7% | 46% | GLM-5.2 leads |
| HLE w/o toolsSource | 40.5% | 30% | GLM-5.2 leads |
| 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 |
MathInkling wins4 benchmarks
Multimodal4 benchmarks
Frequently Asked Questions (5)
Which is better, GLM-5.2 or Inkling?
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 81 to 69. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 63.8%.
Which is better for knowledge tasks, GLM-5.2 or Inkling?
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 51.7. Inside this category, HLE w/o tools is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.2 or Inkling?
Inkling has the edge for coding in this comparison, averaging 68.6 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.2 or Inkling?
Inkling has the edge for math in this comparison, averaging 97.1 versus 95.9. Inside this category, AIME26 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.2 or Inkling?
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 69.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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