Model comparison
GLM-5 vs MiniMax M3
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-5 #15; MiniMax M3 #18
BenchAlign evidence: GLM-5 supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and MiniMax M3 share 18 comparable benchmark results. 3 of 8 categories are comparable. 32 results are unique to GLM-5; 27 to MiniMax M3.
Updated July 16, 2026- Shared results
- 18
- GLM-5 only
- 32
- MiniMax M3 only
- 27
- Comparable categories
- 3 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Confidence note. This is a partial-evidence comparison with 18 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 63. 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 56.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 66%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. That is roughly 2.7x on output cost alone. MiniMax M3 gives you the larger context window at 1M, compared with 200K for GLM-5.
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 | Δ | MiniMax M3 |
|---|---|---|---|
| Math | GLM-556.3 | Margin→ 29.4 | MiniMax M385.7 |
| Agentic | GLM-556.2 | Margin→ 16.1 | MiniMax M372.3 |
| Coding | GLM-566.3 | Margin→ 5.9 | MiniMax M372.2 |
| Reasoning | GLM-560.8 | MarginNo overlap | MiniMax M3Not measured |
| Knowledge | GLM-566.6 | MarginNo overlap | MiniMax M3Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | MiniMax M3Not measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | MiniMax M364.9 |
| Inst. Following | GLM-592.6 | MarginNo overlap | MiniMax M3Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 66%Winner: MiniMax M3Δ 9.8Terminal-Bench 2.0: GLM-5 scored 56.2%; MiniMax M3 scored 66%. MiniMax M3 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 59%Winner: MiniMax M3Δ 3.9SWE-bench Pro: GLM-5 scored 55.1%; MiniMax M3 scored 59%. MiniMax M3 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 77.8%B 80.5%Winner: MiniMax M3Δ 2.7SWE-bench Verified: GLM-5 scored 77.8%; 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-5 | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | MiniMax M3$0.3 input / $1.2 output | MiniMax M3 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M3 wins25 benchmarks
| Benchmark | GLM-5 | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 66% | MiniMax M3 leads |
| Claw-EvalSource | 57.7% | 74.5% | MiniMax M3 leads |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | 74.2% | MiniMax M3 leads |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 88.9% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| BrowseCompSource | — | 83.5% | Not comparable |
| OSWorld-VerifiedSource | — | 70.1% | 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 |
CodingMiniMax M3 wins15 benchmarks
| Benchmark | GLM-5 | MiniMax M3 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 80.5% | MiniMax M3 leads |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 59% | MiniMax M3 leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.2% | 42.4% | GLM-5 leads |
| AA-SciCodeSource | 46.2% | 45.4% | GLM-5 leads |
| 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 |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | GLM-5 | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 82.0% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 27.2% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | 2.0% | 1.4% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 15.0% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins9 benchmarks
| Benchmark | GLM-5 | MiniMax M3 | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| USAMO 2026Source | — | 85.7% | Not comparable |
Multilingual2 benchmarks
Multimodal7 benchmarks
| Benchmark | GLM-5 | MiniMax M3 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1282 | 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 |
Frequently Asked Questions (4)
Which is better, GLM-5 or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 66%.
Which is better for coding, GLM-5 or MiniMax M3?
MiniMax M3 has the edge for coding in this comparison, averaging 72.2 versus 66.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5 or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 56.3. GLM-5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-5 or MiniMax M3?
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 72.3 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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