Model comparison
GLM-5.2 vs MiniMax M2.7
Head-to-head evidence from 22 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); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.2 and MiniMax M2.7 share 22 comparable benchmark results. 2 of 8 categories are comparable. 21 results are unique to GLM-5.2; 13 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 22
- GLM-5.2 only
- 21
- MiniMax M2.7 only
- 13
- Comparable categories
- 2 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. GLM-5.2 only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 22 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiniMax M2.7 has the cleaner BenchAlign overall profile here, landing at 64.11 versus 63.96. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 3.7x on output cost alone. GLM-5.2 is the reasoning model in the pair, while MiniMax M2.7 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. GLM-5.2 gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.
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 | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GLM-5.281.0 | Margin← 24.0 | MiniMax M2.757.0 |
| Coding | GLM-5.262.1 | Margin← 8.8 | MiniMax M2.753.3 |
| Knowledge | GLM-5.259.6 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GLM-5.295.9 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 81%B 57%Winner: GLM-5.2Δ 24Terminal-Bench 2.0: GLM-5.2 scored 81%; MiniMax M2.7 scored 57%. GLM-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 62.1%B 56.2%Winner: GLM-5.2Δ 5.9SWE-bench Pro: GLM-5.2 scored 62.1%; MiniMax M2.7 scored 56.2%. 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 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.2$1.4 input / $4.4 output | MiniMax M2.7$0.3 input / $1.2 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.2Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.2Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.21M | MiniMax M2.7200K | GLM-5.2 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.2 wins21 benchmarks
| Benchmark | GLM-5.2 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 81% | 57% | GLM-5.2 leads |
| MCP AtlasSource | 76.8% | — | Not comparable |
| ToolathlonSource | 48.2% | 46.3% | GLM-5.2 leads |
| AA Agentic IndexSource | 43.1% | 25.6% | GLM-5.2 leads |
| τ²-bench resultsSource | 99.1% | 84.8% | GLM-5.2 leads |
| GDPval-AASource | 50.7% | 32.9% | GLM-5.2 leads |
| GDPval-AASource | 1514 | 1158 | GLM-5.2 leads |
| APEX-Agents-AASource | 33.7% | 10.6% | GLM-5.2 leads |
| 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 | 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 |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingGLM-5.2 wins14 benchmarks
| Benchmark | GLM-5.2 | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench ProSource | 62.1% | 56.2% | GLM-5.2 leads |
| NL2RepoSource | 48.9% | 39.8% | GLM-5.2 leads |
| Terminal-Bench 2.0Source | 81.0% | — | Not comparable |
| ProgramBenchSource | 63.7% | — | Not comparable |
| cursorBench32Source | 55.0% | — | Not comparable |
| AA Coding IndexSource | 68.8% | 52.6% | GLM-5.2 leads |
| AA-SciCodeSource | 50.5% | 47.0% | GLM-5.2 leads |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| SWE MultilingualSource | — | 76.5% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5.2 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 91.2% | — | Not comparable |
| GPQA-DSource | 91.2% | 87.0% | GLM-5.2 leads |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 40.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.1% | 38.1% | GLM-5.2 leads |
| AA-GPQA DiamondSource | 89.5% | 87.4% | GLM-5.2 leads |
| AA-HLESource | 40.1% | 28.1% | GLM-5.2 leads |
| AA-Omniscience IndexSource | 4.0% | 0.7% | GLM-5.2 leads |
| AA-Omniscience AccuracySource | 25.1% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 28.1% | 34.4% | GLM-5.2 leads |
| AA Openness IndexSource | 44.4% | — | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math5 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5.2 | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1340 | 1275 | GLM-5.2 leads |
Inst. Following1 benchmarks
| Benchmark | GLM-5.2 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.3% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, GLM-5.2 or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 63.96. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 57%.
Which is better for coding, GLM-5.2 or MiniMax M2.7?
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 53.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.2 or MiniMax M2.7?
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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