Model comparison
GLM-5.2 vs GPT-5.5
Head-to-head evidence from 36 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.2 #37 (Estimated); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.2 and GPT-5.5 share 36 comparable benchmark results. 4 of 8 categories are comparable. 7 results are unique to GLM-5.2; 21 to GPT-5.5.
Updated July 23, 2026- Shared results
- 36
- GLM-5.2 only
- 7
- GPT-5.5 only
- 21
- Comparable categories
- 4 / 8
Pick GPT-5.5 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 36 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
GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 63.96. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 81. The single biggest benchmark swing on the page is SWE-bench Pro, 62.1% to 58.6%. GLM-5.2 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 6.8x on output cost alone.
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 | GPT-5.5 |
|---|---|---|---|
| Math | GLM-5.295.9 | Margin← 48.3 | GPT-5.547.6 |
| Coding | GLM-5.262.1 | Margin← 3.5 | GPT-5.558.6 |
| Knowledge | GLM-5.259.6 | Margin← 1.8 | GPT-5.557.8 |
| Agentic | GLM-5.281.0 | Margin→ 0.6 | GPT-5.581.6 |
| Reasoning | GLM-5.2Not measured | MarginNo overlap | GPT-5.585.0 |
| Multimodal | GLM-5.2Not measured | MarginNo overlap | GPT-5.570.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 62.1%B 58.6%Winner: GLM-5.2Δ 3.5SWE-bench Pro: GLM-5.2 scored 62.1%; GPT-5.5 scored 58.6%. GLM-5.2 wins this benchmark. - Source ↗
HLE
KnowledgeA 54.7%B 52.2%Winner: GLM-5.2Δ 2.5HLE: GLM-5.2 scored 54.7%; GPT-5.5 scored 52.2%. GLM-5.2 wins this benchmark. - Source ↗
GPQA
KnowledgeA 91.2%B 93.6%Winner: GPT-5.5Δ 2.4GPQA: GLM-5.2 scored 91.2%; GPT-5.5 scored 93.6%. GPT-5.5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 81%B 82%Winner: GPT-5.5Δ 1Terminal-Bench 2.0: GLM-5.2 scored 81%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.2 | GPT-5.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.2$1.4 input / $4.4 output | GPT-5.5$5 input / $30 output | GLM-5.2 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.2Not available | GPT-5.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.2Not available | GPT-5.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.21M | GPT-5.51M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.5 wins24 benchmarks
| Benchmark | GLM-5.2 | GPT-5.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 81% | 82% | GPT-5.5 leads |
| MCP AtlasSource | 76.8% | 75.3% | GLM-5.2 leads |
| ToolathlonSource | 48.2% | 55.6% | GPT-5.5 leads |
| AA Agentic IndexSource | 43.1% | 44.9% | GPT-5.5 leads |
| τ²-bench resultsSource | 99.1% | 93.9% | GLM-5.2 leads |
| GDPval-AASource | 50.7% | 49.5% | GLM-5.2 leads |
| GDPval-AASource | 1514 | 1490 | GLM-5.2 leads |
| APEX-Agents-AASource | 33.7% | 37.7% | GPT-5.5 leads |
| ResearchClawBenchSource | 20.7% | 17.0% | GLM-5.2 leads |
| AA BriefcaseSource | 1260 | 1154 | GLM-5.2 leads |
| AA AutomationBenchSource | 27.8% | 42.1% | GPT-5.5 leads |
| AA EnterpriseOps-GymSource | 42.7% | 46.6% | GPT-5.5 leads |
| AA Harvey LABSource | 91.0% | 86.3% | GLM-5.2 leads |
| AA ITBenchSource | 42.7% | 45.8% | GPT-5.5 leads |
| AA Tau3 BankingSource | 26.8% | 31.3% | GPT-5.5 leads |
| terminalBenchHardSource | 50.8% | 60.6% | GPT-5.5 leads |
| aaTerminalBench21Source | 77.9% | 84.3% | GPT-5.5 leads |
| CyberGymSource | — | 81.8% | Not comparable |
| BrowseCompSource | — | 84.4% | Not comparable |
| OSWorld-VerifiedSource | — | 78.7% | Not comparable |
| Gert LabsSource | — | 72.93% | Not comparable |
| OSWorld 2.0Source | — | 13.0% | Not comparable |
| JobBenchSource | — | 42.7% | Not comparable |
| ExploitGymSource | — | 13.4% | Not comparable |
CodingGLM-5.2 wins11 benchmarks
| Benchmark | GLM-5.2 | GPT-5.5 | Result |
|---|---|---|---|
| SWE-bench ProSource | 62.1% | 58.6% | GLM-5.2 leads |
| NL2RepoSource | 48.9% | — | Not comparable |
| Terminal-Bench 2.0Source | 81.0% | 82.0% | GPT-5.5 leads |
| ProgramBenchSource | 63.7% | — | Not comparable |
| cursorBench32Source | 55.0% | 58.4% | GPT-5.5 leads |
| AA Coding IndexSource | 68.8% | 74.9% | GPT-5.5 leads |
| AA-SciCodeSource | 50.5% | 56.1% | GPT-5.5 leads |
| Vibe Code BenchSource | — | 69.85% | Not comparable |
| React Native EvalsSource | — | 84.7% | Not comparable |
| cursorBench31Source | — | 59.2% | Not comparable |
| FrontierCode 1.1 MainSource | — | 43.0% | Not comparable |
Reasoning5 benchmarks
KnowledgeGLM-5.2 wins11 benchmarks
| Benchmark | GLM-5.2 | GPT-5.5 | Result |
|---|---|---|---|
| GPQASource | 91.2% | 93.6% | GPT-5.5 leads |
| GPQA-DSource | 91.2% | 93.6% | GPT-5.5 leads |
| HLESource | 54.7% | 52.2% | GLM-5.2 leads |
| HLE w/o toolsSource | 40.5% | 41.4% | GPT-5.5 leads |
| Artificial Analysis Intelligence IndexSource | 51.1% | 54.8% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 89.5% | 93.5% | GPT-5.5 leads |
| AA-HLESource | 40.1% | 44.3% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 4.0% | 20.1% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 25.1% | 56.9% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 28.1% | 85.5% | GLM-5.2 leads |
| AA Openness IndexSource | 44.4% | — | Not comparable |
MathGLM-5.2 wins7 benchmarks
| Benchmark | GLM-5.2 | GPT-5.5 | Result |
|---|---|---|---|
| AIME26Source | 99.2% | — | Not comparable |
| HMMT Nov 2025Source | 94.4% | — | Not comparable |
| HMMT Feb 2026Source | 92.5% | — | Not comparable |
| MMAnswerBenchSource | 91.0% | — | Not comparable |
| FrontierMath (legacy)Source | — | 51.7% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 51.700% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 35.400% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-5.2 | GPT-5.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.3% | 75.9% | GPT-5.5 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.2 or GPT-5.5?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 63.96. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 62.1% and 58.6%.
Which is better for knowledge tasks, GLM-5.2 or GPT-5.5?
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 57.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.2 or GPT-5.5?
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 58.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.2 or GPT-5.5?
GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 47.6. GPT-5.5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-5.2 or GPT-5.5?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 81. Inside this category, AA Briefcase is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.