Model comparison
GLM-5 vs MiMo-V2.5-Pro
Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); MiMo-V2.5-Pro #14 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and MiMo-V2.5-Pro share 19 comparable benchmark results. 3 of 8 categories are comparable. 30 results are unique to GLM-5; 12 to MiMo-V2.5-Pro.
Updated July 21, 2026- Shared results
- 19
- GLM-5 only
- 30
- MiMo-V2.5-Pro only
- 12
- Comparable categories
- 3 / 8
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. GLM-5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 19 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
MiMo-V2.5-Pro is clearly ahead on the BenchAlign aggregate, 70.19 to 66.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5-Pro's sharpest advantage is in agentic, where it averages 68.4 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 68.4%. GLM-5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5-Pro is the reasoning model in the pair, while GLM-5 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. MiMo-V2.5-Pro 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 | MiMo-V2.5-Pro |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin← 18.4 | MiMo-V2.5-Pro48.0 |
| Agentic | GLM-556.2 | Margin→ 12.2 | MiMo-V2.5-Pro68.4 |
| Coding | GLM-566.3 | Margin← 9.1 | MiMo-V2.5-Pro57.2 |
| Reasoning | GLM-560.8 | MarginNo overlap | MiMo-V2.5-ProNot measured |
| Math | GLM-556.3 | MarginNo overlap | MiMo-V2.5-ProNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | MiMo-V2.5-ProNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | MiMo-V2.5-ProNot 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 68.4%Winner: MiMo-V2.5-ProΔ 12.2Terminal-Bench 2.0: GLM-5 scored 56.2%; MiMo-V2.5-Pro scored 68.4%. MiMo-V2.5-Pro wins this benchmark. - Source ↗
HLE
KnowledgeA 50.4%B 48%Winner: GLM-5Δ 2.4HLE: GLM-5 scored 50.4%; MiMo-V2.5-Pro scored 48%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 57.2%Winner: MiMo-V2.5-ProΔ 2.1SWE-bench Pro: GLM-5 scored 55.1%; MiMo-V2.5-Pro scored 57.2%. MiMo-V2.5-Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | MiMo-V2.5-Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | MiMo-V2.5-ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | MiMo-V2.5-ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | MiMo-V2.5-ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | MiMo-V2.5-Pro1M | MiMo-V2.5-Pro lists the larger context window. |
Benchmark Deep Dive
AgenticMiMo-V2.5-Pro wins21 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 68.4% | MiMo-V2.5-Pro leads |
| Claw-EvalSource | 57.7% | 63.8% | MiMo-V2.5-Pro leads |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | 72.9% | MiMo-V2.5-Pro leads |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 94.2% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | 2.4% | GLM-5 leads |
| Gert LabsSource | 50.99% | 62.70% | MiMo-V2.5-Pro leads |
| GDPval-AASource | — | 1265 | Not comparable |
| AA Agentic IndexSource | — | 29.1% | Not comparable |
| GDPval-AASource | — | 38.3% | Not comparable |
| AA BriefcaseSource | — | 873 | Not comparable |
| AA ITBenchSource | — | 38.2% | Not comparable |
| terminalBenchHardSource | — | 43.2% | Not comparable |
| aaTerminalBench21Source | — | 65.2% | Not comparable |
| AA Harvey LABSource | — | 73.3% | Not comparable |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 57.2% | MiMo-V2.5-Pro leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 50.2% | MiMo-V2.5-Pro leads |
| Terminal-Bench 2.0Source | — | 68.4% | Not comparable |
| AA Coding IndexSource | — | 60.2% | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins14 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5-Pro | 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% | 48% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 39.5% | 42.2% | MiMo-V2.5-Pro leads |
| AA-GPQA DiamondSource | 82.0% | 86.6% | MiMo-V2.5-Pro leads |
| AA-HLESource | 27.2% | 33.8% | MiMo-V2.5-Pro leads |
| AA-Omniscience IndexSource | 2.0% | 3.6% | MiMo-V2.5-Pro leads |
| AA-Omniscience AccuracySource | 26.9% | 22.6% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 24.5% | MiMo-V2.5-Pro leads |
| HLE w/o toolsSource | — | 34% | Not comparable |
| AA Openness IndexSource | — | 38.9% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5-Pro | 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 |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | 1298 | MiMo-V2.5-Pro leads |
Frequently Asked Questions (4)
Which is better, GLM-5 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is ahead on BenchLM's BenchAlign leaderboard, 70.19 to 66.06. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 68.4%.
Which is better for knowledge tasks, GLM-5 or MiMo-V2.5-Pro?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 48. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or MiMo-V2.5-Pro?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 57.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro has the edge for agentic tasks in this comparison, averaging 68.4 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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