Model comparison
Claude Opus 4.6 vs GLM-5.2
Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.6 #16 (Supported); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.6 and GLM-5.2 share 19 comparable benchmark results. 4 of 8 categories are comparable. 27 results are unique to Claude Opus 4.6; 24 to GLM-5.2.
Updated July 23, 2026- Shared results
- 19
- Claude Opus 4.6 only
- 27
- GLM-5.2 only
- 24
- Comparable categories
- 4 / 8
Pick Claude Opus 4.6 if you want the stronger benchmark profile. GLM-5.2 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 6 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
Claude Opus 4.6 is clearly ahead on the BenchAlign aggregate, 68.59 to 63.96. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Opus 4.6's sharpest advantage is in knowledge, where it averages 69.1 against 59.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 65.4% to 81%. GLM-5.2 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Claude Opus 4.6 is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 5.7x on output cost alone. GLM-5.2 is the reasoning model in the pair, while Claude Opus 4.6 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 | Claude Opus 4.6 | Δ | GLM-5.2 |
|---|---|---|---|
| Math | Claude Opus 4.636.3 | Margin→ 59.6 | GLM-5.295.9 |
| Knowledge | Claude Opus 4.669.1 | Margin← 9.5 | GLM-5.259.6 |
| Agentic | Claude Opus 4.673.0 | Margin→ 8.0 | GLM-5.281.0 |
| Coding | Claude Opus 4.668.1 | Margin← 6.0 | GLM-5.262.1 |
| Multimodal | Claude Opus 4.677.3 | MarginNo overlap | GLM-5.2Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 65.4%B 81%Winner: GLM-5.2Δ 15.6Terminal-Bench 2.0: Claude Opus 4.6 scored 65.4%; GLM-5.2 scored 81%. GLM-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 53.4%B 62.1%Winner: GLM-5.2Δ 8.7SWE-bench Pro: Claude Opus 4.6 scored 53.4%; GLM-5.2 scored 62.1%. GLM-5.2 wins this benchmark. - Source ↗
HLE
KnowledgeA 53%B 54.7%Winner: GLM-5.2Δ 1.7HLE: Claude Opus 4.6 scored 53%; GLM-5.2 scored 54.7%. GLM-5.2 wins this benchmark. - Source ↗
GPQA
KnowledgeA 91.3%B 91.2%Winner: Claude Opus 4.6Δ 0.1GPQA: Claude Opus 4.6 scored 91.3%; GLM-5.2 scored 91.2%. Claude Opus 4.6 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.6 | GLM-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.6$5 input / $25 output | GLM-5.2$1.4 input / $4.4 output | GLM-5.2 has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.640 tok/s | GLM-5.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.61.78 s | GLM-5.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.61M | GLM-5.21M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGLM-5.2 wins24 benchmarks
| Benchmark | Claude Opus 4.6 | GLM-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 65.4% | 81% | GLM-5.2 leads |
| BrowseCompSource | 83.7% | — | Not comparable |
| OSWorld-VerifiedSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | 99.1% | GLM-5.2 leads |
| Claw-EvalSource | 70.4% | — | Not comparable |
| DeepSearchQASource | 73.7% | — | Not comparable |
| CyberGymSource | 66.6% | — | Not comparable |
| Gert LabsSource | 61.85% | — | Not comparable |
| ResearchClawBenchSource | 19.9% | 20.7% | GLM-5.2 leads |
| JobBenchSource | 36.7% | — | Not comparable |
| MCP AtlasSource | — | 76.8% | Not comparable |
| ToolathlonSource | — | 48.2% | Not comparable |
| AA Agentic IndexSource | — | 43.1% | Not comparable |
| GDPval-AASource | — | 50.7% | Not comparable |
| GDPval-AASource | — | 1514 | Not comparable |
| APEX-Agents-AASource | — | 33.7% | Not comparable |
| AA BriefcaseSource | — | 1260 | Not comparable |
| AA AutomationBenchSource | — | 27.8% | 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 |
CodingClaude Opus 4.6 wins14 benchmarks
| Benchmark | Claude Opus 4.6 | GLM-5.2 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.8% | — | Not comparable |
| SWE-bench Verified*Source | 75.6% | — | Not comparable |
| LiveCodeBench ProSource | 70.7% | — | Not comparable |
| SWE-bench ProSource | 53.4% | 62.1% | GLM-5.2 leads |
| SWE-RebenchSource | 65.3% | — | Not comparable |
| React Native EvalsSource | 84.1% | — | Not comparable |
| Vibe Code BenchSource | 57.57% | — | Not comparable |
| AA-SciCodeSource | 45.7% | 50.5% | GLM-5.2 leads |
| FrontierCode 1.1 MainSource | 26.9% | — | 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 |
Reasoning2 benchmarks
KnowledgeClaude Opus 4.6 wins16 benchmarks
| Benchmark | Claude Opus 4.6 | GLM-5.2 | Result |
|---|---|---|---|
| GPQASource | 91.3% | 91.2% | Claude Opus 4.6 leads |
| GPQA-DSource | 89.2% | 91.2% | GLM-5.2 leads |
| SuperGPQASource | 95% | — | Not comparable |
| MMLU-ProSource | 82% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 89.1% | — | Not comparable |
| HLESource | 53% | 54.7% | GLM-5.2 leads |
| HLE w/o toolsSource | 40% | 40.5% | GLM-5.2 leads |
| HealthBench HardSource | 14.8% | — | Not comparable |
| MedXpertQA (Text)Source | 52.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.8% | 51.1% | GLM-5.2 leads |
| AA-GPQA DiamondSource | 84.0% | 89.5% | GLM-5.2 leads |
| AA-HLESource | 18.6% | 40.1% | GLM-5.2 leads |
| AA-Omniscience IndexSource | 3.5% | 4.0% | GLM-5.2 leads |
| AA-Omniscience AccuracySource | 45.2% | 25.1% | Claude Opus 4.6 leads |
| AA-Omniscience Hallucination RateSource | 76.0% | 28.1% | GLM-5.2 leads |
| AA Openness IndexSource | — | 44.4% | Not comparable |
MathGLM-5.2 wins7 benchmarks
| Benchmark | Claude Opus 4.6 | GLM-5.2 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | 99.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 40.700% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 22.900% | — | Not comparable |
| AIME26Source | — | 99.2% | Not comparable |
| HMMT Nov 2025Source | — | 94.4% | Not comparable |
| HMMT Feb 2026Source | — | 92.5% | Not comparable |
| MMAnswerBenchSource | — | 91.0% | Not comparable |
Multimodal6 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.6 | GLM-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 44.6% | 73.3% | GLM-5.2 leads |
Frequently Asked Questions (5)
Which is better, Claude Opus 4.6 or GLM-5.2?
Claude Opus 4.6 is ahead on BenchLM's BenchAlign leaderboard, 68.59 to 63.96. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 65.4% and 81%.
Which is better for knowledge tasks, Claude Opus 4.6 or GLM-5.2?
Claude Opus 4.6 has the edge for knowledge tasks in this comparison, averaging 69.1 versus 59.6. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Opus 4.6 or GLM-5.2?
Claude Opus 4.6 has the edge for coding in this comparison, averaging 68.1 versus 62.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, Claude Opus 4.6 or GLM-5.2?
GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 36.3. Claude Opus 4.6 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Claude Opus 4.6 or GLM-5.2?
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 73. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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