Model comparison
GLM-4.7 vs MiniMax M3
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.7 #32; MiniMax M3 #18
BenchAlign evidence: GLM-4.7 supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and MiniMax M3 share 20 comparable benchmark results. 3 of 8 categories are comparable. 11 results are unique to GLM-4.7; 25 to MiniMax M3.
Updated July 16, 2026- Shared results
- 20
- GLM-4.7 only
- 11
- MiniMax M3 only
- 25
- Comparable categories
- 3 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 20 shared benchmark results across 6 evidence categories; 3 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 62. 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 1.8. The single biggest benchmark swing on the page is BrowseComp, 52% to 83.5%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiniMax M3 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 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-4.7.
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-4.7 | Δ | MiniMax M3 |
|---|---|---|---|
| Math | GLM-4.71.8 | Margin→ 83.9 | MiniMax M385.7 |
| Agentic | GLM-4.745.7 | Margin→ 26.6 | MiniMax M372.3 |
| Coding | GLM-4.775.4 | Margin← 3.2 | MiniMax M372.2 |
| Knowledge | GLM-4.752.1 | MarginNo overlap | MiniMax M3Not measured |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | MiniMax M364.9 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
BrowseComp
AgenticA 52%B 83.5%Winner: MiniMax M3Δ 31.5BrowseComp: GLM-4.7 scored 52%; MiniMax M3 scored 83.5%. MiniMax M3 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 41%B 66%Winner: MiniMax M3Δ 25Terminal-Bench 2.0: GLM-4.7 scored 41%; MiniMax M3 scored 66%. MiniMax M3 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 80.5%Winner: MiniMax M3Δ 6.7SWE-bench Verified: GLM-4.7 scored 73.8%; MiniMax M3 scored 80.5%. MiniMax M3 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | MiniMax M3$0.3 input / $1.2 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M3 wins18 benchmarks
| Benchmark | GLM-4.7 | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 66% | MiniMax M3 leads |
| BrowseCompSource | 52% | 83.5% | MiniMax M3 leads |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 35.4% | MiniMax M3 leads |
| τ²-bench resultsSource | 95.9% | 88.9% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 44.7% | MiniMax M3 leads |
| GDPval-AASource | 1165 | 1395 | MiniMax M3 leads |
| OSWorld-VerifiedSource | — | 70.1% | Not comparable |
| MCP AtlasSource | — | 74.2% | Not comparable |
| Claw-EvalSource | — | 74.5% | 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 |
CodingGLM-4.7 wins14 benchmarks
| Benchmark | GLM-4.7 | MiniMax M3 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 80.5% | MiniMax M3 leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 58.6% | MiniMax M3 leads |
| Terminal-Bench HardSource | 31.8% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 45.1% | 45.4% | MiniMax M3 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SWE-bench ProSource | — | 59% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.0% | Not comparable |
| NL2RepoSource | — | 42.1% | 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
Knowledge10 benchmarks
| Benchmark | GLM-4.7 | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 85.9% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 25.1% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -34.6% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 29.3% | 15.0% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins4 benchmarks
Multimodal7 benchmarks
| Benchmark | GLM-4.7 | MiniMax M3 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | 1294 | MiniMax M3 leads |
| 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 |
| AA-MMMU-ProSource | — | 78.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (4)
Which is better, GLM-4.7 or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 62. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 83.5%.
Which is better for coding, GLM-4.7 or MiniMax M3?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 72.2. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-4.7 or MiniMax M3?
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 72.3 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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