Model comparison
GLM-4.6 vs MiniMax M2.7
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.6 #85 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.6 and MiniMax M2.7 share 12 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to GLM-4.6; 23 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 12
- GLM-4.6 only
- 2
- MiniMax M2.7 only
- 23
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.6 and MiniMax M2.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
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-4.6 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GLM-4.6Not measured | MarginNo overlap | MiniMax M2.757.0 |
| Coding | GLM-4.6Not measured | MarginNo overlap | MiniMax M2.753.3 |
| Math | GLM-4.63.4 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.6 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.6Not available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.6Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.6Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.6200K | MiniMax M2.7200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | GLM-4.6 | MiniMax M2.7 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 76.9% | 84.8% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | — | 57% | Not comparable |
| ToolathlonSource | — | 46.3% | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| AA Agentic IndexSource | — | 25.6% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| GDPval-AASource | — | 32.9% | Not comparable |
| GDPval-AASource | — | 1158 | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
Coding11 benchmarks
| Benchmark | GLM-4.6 | MiniMax M2.7 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 3.09% | 27.04% | MiniMax M2.7 leads |
| AA-SciCodeSource | 33.1% | 47.0% | MiniMax M2.7 leads |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-bench ProSource | — | 56.2% | 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 |
| NL2RepoSource | — | 39.8% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
| AA Coding IndexSource | — | 52.6% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GLM-4.6 | MiniMax M2.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 23.0% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 63.2% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 5.2% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -31.6% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 20.8% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 66.1% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.6 | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.6 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 36.7% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Can I compare GLM-4.6 and MiniMax M2.7 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-4.6 and MiniMax M2.7 today?
MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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