Model comparison
GLM-5.1 vs GLM-5.2
Head-to-head evidence from 27 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Sibling matchup inside the GLM-5 family.
Public leaderboard positions: GLM-5.1 #18 (Supported); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and GLM-5.2 share 27 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to GLM-5.1; 16 to GLM-5.2.
Updated July 23, 2026- Shared results
- 27
- GLM-5.1 only
- 9
- GLM-5.2 only
- 16
- Comparable categories
- 4 / 8
GLM-5.1 makes more sense if you want this variant’s specific profile, while GLM-5.2 is the cleaner fit if mathematics is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 27 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 and GLM-5.2 sit in the same GLM-5 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GLM-5.1 is clearly ahead on the BenchAlign aggregate, 67.74 to 63.96. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2 gives you the larger context window at 1M, 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 | GLM-5.2 |
|---|---|---|---|
| Math | GLM-5.162.0 | Margin→ 33.9 | GLM-5.295.9 |
| Agentic | GLM-5.165.4 | Margin→ 15.6 | GLM-5.281.0 |
| Knowledge | GLM-5.152.3 | Margin→ 7.3 | GLM-5.259.6 |
| Coding | GLM-5.161.3 | Margin→ 0.8 | GLM-5.262.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 81%Winner: GLM-5.2Δ 17.5Terminal-Bench 2.0: GLM-5.1 scored 63.5%; GLM-5.2 scored 81%. GLM-5.2 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 82.6%B 92.5%Winner: GLM-5.2Δ 9.9HMMT Feb 2026: GLM-5.1 scored 82.6%; GLM-5.2 scored 92.5%. GLM-5.2 wins this benchmark. - Source ↗
AIME26
MathA 95.3%B 99.2%Winner: GLM-5.2Δ 3.9AIME26: GLM-5.1 scored 95.3%; GLM-5.2 scored 99.2%. GLM-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.4%B 62.1%Winner: GLM-5.2Δ 3.7SWE-bench Pro: GLM-5.1 scored 58.4%; GLM-5.2 scored 62.1%. GLM-5.2 wins this benchmark. - Source ↗
HLE
KnowledgeA 52.3%B 54.7%Winner: GLM-5.2Δ 2.4HLE: GLM-5.1 scored 52.3%; GLM-5.2 scored 54.7%. 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-5.1 | GLM-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | GLM-5.2$1.4 input / $4.4 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-5.1Not available | GLM-5.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | GLM-5.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | GLM-5.21M | GLM-5.2 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.2 wins22 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 81% | GLM-5.2 leads |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | 76.8% | GLM-5.2 leads |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | 43.1% | GLM-5.2 leads |
| τ²-bench resultsSource | 97.7% | 99.1% | GLM-5.2 leads |
| GDPval-AASource | 37.8% | 50.7% | GLM-5.2 leads |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | 1514 | GLM-5.2 leads |
| ResearchClawBenchSource | 18.2% | 20.7% | GLM-5.2 leads |
| ToolathlonSource | — | 48.2% | 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 |
CodingGLM-5.2 wins9 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 62.1% | GLM-5.2 leads |
| NL2RepoSource | 42.7% | 48.9% | GLM-5.2 leads |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | — | Not comparable |
| AA Coding IndexSource | 55.8% | 68.8% | GLM-5.2 leads |
| AA-SciCodeSource | 43.8% | 50.5% | GLM-5.2 leads |
| Terminal-Bench 2.0Source | — | 81.0% | Not comparable |
| ProgramBenchSource | — | 63.7% | Not comparable |
| cursorBench32Source | — | 55.0% | Not comparable |
Reasoning2 benchmarks
KnowledgeGLM-5.2 wins11 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | 91.2% | GLM-5.2 leads |
| HLESource | 52.3% | 54.7% | GLM-5.2 leads |
| Artificial Analysis Intelligence IndexSource | 40.2% | 51.1% | GLM-5.2 leads |
| AA-GPQA DiamondSource | 86.8% | 89.5% | GLM-5.2 leads |
| AA-HLESource | 28.0% | 40.1% | GLM-5.2 leads |
| AA-Omniscience IndexSource | 1.9% | 4.0% | GLM-5.2 leads |
| AA-Omniscience AccuracySource | 24.2% | 25.1% | GLM-5.2 leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 28.1% | GLM-5.2 leads |
| GPQASource | — | 91.2% | Not comparable |
| HLE w/o toolsSource | — | 40.5% | Not comparable |
| AA Openness IndexSource | — | 44.4% | Not comparable |
MathGLM-5.2 wins6 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| AIME26Source | 95.3% | 99.2% | GLM-5.2 leads |
| HMMT Nov 2025Source | 94.0% | 94.4% | GLM-5.2 leads |
| HMMT Feb 2026Source | 82.6% | 92.5% | GLM-5.2 leads |
| MMAnswerBenchSource | 83.8% | 91.0% | GLM-5.2 leads |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 12.500% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | 1340 | GLM-5.2 leads |
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | GLM-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 73.3% | GLM-5.1 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or GLM-5.2?
GLM-5.1 and GLM-5.2 are sibling variants in the GLM-5 family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard 67.74 to 63.96.
Which is better for knowledge tasks, GLM-5.1 or GLM-5.2?
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 52.3. Inside this category, AA-HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or GLM-5.2?
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 61.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or GLM-5.2?
GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 62. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or GLM-5.2?
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 65.4. Inside this category, GDPval-AA 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.
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.