Model comparison
GLM-4.6 vs MiniMax M3
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.6 unranked; MiniMax M3 #18
BenchAlign evidence: GLM-4.6 supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.6 and MiniMax M3 share 12 comparable benchmark results. 1 of 8 categories are comparable. 3 results are unique to GLM-4.6; 33 to MiniMax M3.
Updated July 16, 2026- Shared results
- 12
- GLM-4.6 only
- 3
- MiniMax M3 only
- 33
- Comparable categories
- 1 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. GLM-4.6 only becomes the better choice if you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiniMax M3 is clearly ahead on the provisional aggregate, 70 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M3's sharpest advantage is in mathematics, where it averages 85.7 against 3.4.
GLM-4.6 is the reasoning model in the pair, while MiniMax M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 200K for GLM-4.6.
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 M3 |
|---|---|---|---|
| Math | GLM-4.63.4 | Margin→ 82.3 | MiniMax M385.7 |
| Agentic | GLM-4.6Not measured | MarginNo overlap | MiniMax M372.3 |
| Coding | GLM-4.6Not measured | MarginNo overlap | MiniMax M372.2 |
| Multimodal | GLM-4.6Not measured | MarginNo overlap | MiniMax M364.9 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.6 | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.6Not available | MiniMax M3$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.6Not available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.6Not available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.6200K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-4.6 | MiniMax M3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 76.9% | 88.9% | MiniMax M3 leads |
| Terminal-Bench 2.0Source | — | 66% | Not comparable |
| BrowseCompSource | — | 83.5% | Not comparable |
| OSWorld-VerifiedSource | — | 70.1% | Not comparable |
| MCP AtlasSource | — | 74.2% | Not comparable |
| Claw-EvalSource | — | 74.5% | Not comparable |
| AA Agentic IndexSource | — | 35.4% | Not comparable |
| GDPval-AASource | — | 44.7% | Not comparable |
| GDPval-AASource | — | 1395 | Not comparable |
| GDPval rubricsSource | — | 74.7% | Not comparable |
| BankerToolBenchSource | — | 76.1% | Not comparable |
| ResearchClawBenchSource | — | 19.8% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 1110 | Not comparable |
| AA EnterpriseOps-GymSource | — | 32.1% | Not comparable |
| AA Harvey LABSource | — | 6.7% | Not comparable |
Coding12 benchmarks
| Benchmark | GLM-4.6 | MiniMax M3 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 3.09% | — | Not comparable |
| Terminal-Bench HardSource | 28.8% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 33.1% | 45.4% | MiniMax M3 leads |
| SWE-bench VerifiedSource | — | 80.5% | Not comparable |
| SWE-bench ProSource | — | 59% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.0% | Not comparable |
| NL2RepoSource | — | 42.1% | Not comparable |
| AA Coding IndexSource | — | 58.6% | Not comparable |
| VIBE V2Source | — | 50.1% | Not comparable |
| SVG-BenchSource | — | 63.7% | Not comparable |
| KernelBench HardSource | — | 28.8% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 65.2% | Not comparable |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | GLM-4.6 | MiniMax M3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 23.0% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 63.2% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 5.2% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -31.6% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 20.8% | 15.0% | GLM-4.6 leads |
| AA-Omniscience Hallucination RateSource | 66.1% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins3 benchmarks
Multimodal7 benchmarks
| Benchmark | GLM-4.6 | MiniMax M3 | Result |
|---|---|---|---|
| OfficeQA ProSource | — | 45.1% | Not comparable |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| MMMU-ProSource | — | 78.1% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
| Design Arena WebsiteSource | — | 1294 | Not comparable |
| AA-MMMU-ProSource | — | 78.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.6 | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 36.7% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (2)
Which is better, GLM-4.6 or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 45.
Which is better for math, GLM-4.6 or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 3.4. GLM-4.6 stays close enough that the answer can still flip depending on your workload.
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