Model comparison
GLM-5.1 vs GPT-5.2
Head-to-head evidence from 18 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); GPT-5.2 #64 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and GPT-5.2 share 18 comparable benchmark results. 4 of 8 categories are comparable. 18 results are unique to GLM-5.1; 10 to GPT-5.2.
Updated July 23, 2026- Shared results
- 18
- GLM-5.1 only
- 18
- GPT-5.2 only
- 10
- Comparable categories
- 4 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. GPT-5.2 only becomes the better choice if knowledge is the priority or you need the larger 400K context window.
Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 7 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-5.1 is clearly ahead on the BenchAlign aggregate, 67.74 to 58.43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in mathematics, where it averages 62 against 35.2. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 33.448% to 40.700%. GPT-5.2 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 3.2x on output cost alone. GPT-5.2 gives you the larger context window at 400K, compared with 203K for GLM-5.1.
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-5.1 | Δ | GPT-5.2 |
|---|---|---|---|
| Knowledge | GLM-5.152.3 | Margin→ 40.1 | GPT-5.292.4 |
| Math | GLM-5.162.0 | Margin← 26.8 | GPT-5.235.2 |
| Agentic | GLM-5.165.4 | Margin← 9.7 | GPT-5.255.7 |
| Coding | GLM-5.161.3 | Margin→ 9.3 | GPT-5.270.6 |
| Reasoning | GLM-5.1Not measured | MarginNo overlap | GPT-5.252.9 |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | GPT-5.280.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 33.448%B 40.700%Winner: GPT-5.2Δ 7.3FrontierMath v2 (Tiers 1-3): GLM-5.1 scored 33.448%; GPT-5.2 scored 40.700%. GPT-5.2 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 12.500%B 18.800%Winner: GPT-5.2Δ 6.3FrontierMath v2 (Tier 4): GLM-5.1 scored 12.500%; GPT-5.2 scored 18.800%. GPT-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.4%B 55.6%Winner: GLM-5.1Δ 2.8SWE-bench Pro: GLM-5.1 scored 58.4%; GPT-5.2 scored 55.6%. GLM-5.1 wins this benchmark. - Source ↗
BrowseComp
AgenticA 68%B 65.8%Winner: GLM-5.1Δ 2.2BrowseComp: GLM-5.1 scored 68%; GPT-5.2 scored 65.8%. GLM-5.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | GPT-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | GPT-5.2$1.75 input / $14 output | GLM-5.1 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | GPT-5.273 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | GPT-5.2130.34 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | GPT-5.2400K | GPT-5.2 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.1 wins14 benchmarks
| Benchmark | GLM-5.1 | GPT-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | — | Not comparable |
| BrowseCompSource | 68% | 65.8% | GLM-5.1 leads |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | — | Not comparable |
| τ²-bench resultsSource | 97.7% | 84.8% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | — | Not comparable |
| Gert LabsSource | 60.11% | 46.54% | GLM-5.1 leads |
| GDPval-AASource | 1257 | — | Not comparable |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| OSWorld-VerifiedSource | — | 47.3% | Not comparable |
| JobBenchSource | — | 34.3% | Not comparable |
CodingGPT-5.2 wins7 benchmarks
| Benchmark | GLM-5.1 | GPT-5.2 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 55.6% | GLM-5.1 leads |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | 53.50% | GPT-5.2 leads |
| AA Coding IndexSource | 55.8% | — | Not comparable |
| AA-SciCodeSource | 43.8% | 52.1% | GPT-5.2 leads |
| SWE-bench VerifiedSource | — | 80% | Not comparable |
Reasoning3 benchmarks
KnowledgeGPT-5.2 wins9 benchmarks
| Benchmark | GLM-5.1 | GPT-5.2 | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | — | Not comparable |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | 42.2% | GPT-5.2 leads |
| AA-GPQA DiamondSource | 86.8% | 90.3% | GPT-5.2 leads |
| AA-HLESource | 28.0% | 35.4% | GPT-5.2 leads |
| AA-Omniscience IndexSource | 1.9% | -1.0% | GLM-5.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 43.8% | GPT-5.2 leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 79.7% | GLM-5.1 leads |
| GPQASource | — | 92.4% | Not comparable |
MathGLM-5.1 wins7 benchmarks
| Benchmark | GLM-5.1 | GPT-5.2 | Result |
|---|---|---|---|
| AIME26Source | 95.3% | — | Not comparable |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | — | Not comparable |
| MMAnswerBenchSource | 83.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | 40.700% | GPT-5.2 leads |
| FrontierMath v2 (Tier 4)Source | 12.500% | 18.800% | GPT-5.2 leads |
| AA AIME 2025Source | — | 99.0% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | GPT-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 75.4% | GLM-5.1 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or GPT-5.2?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 58.43. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 33.448% and 40.700%.
Which is better for knowledge tasks, GLM-5.1 or GPT-5.2?
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or GPT-5.2?
GPT-5.2 has the edge for coding in this comparison, averaging 70.6 versus 61.3. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or GPT-5.2?
GLM-5.1 has the edge for math in this comparison, averaging 62 versus 35.2. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or GPT-5.2?
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 55.7. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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