Model comparison
GLM-4.7 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-4.7 #42 (Supported); MiMo-V2.5-Pro #14 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and MiMo-V2.5-Pro share 19 comparable benchmark results. 3 of 8 categories are comparable. 11 results are unique to GLM-4.7; 12 to MiMo-V2.5-Pro.
Updated July 21, 2026- Shared results
- 19
- GLM-4.7 only
- 11
- MiMo-V2.5-Pro only
- 12
- Comparable categories
- 3 / 8
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority.
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 61.16. 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 45.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 68.4%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5-Pro 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 | MiMo-V2.5-Pro |
|---|---|---|---|
| Agentic | GLM-4.745.7 | Margin→ 22.7 | MiMo-V2.5-Pro68.4 |
| Coding | GLM-4.775.4 | Margin← 18.2 | MiMo-V2.5-Pro57.2 |
| Knowledge | GLM-4.751.8 | Margin← 3.8 | MiMo-V2.5-Pro48.0 |
| Math | GLM-4.71.8 | 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 41%B 68.4%Winner: MiMo-V2.5-ProΔ 27.4Terminal-Bench 2.0: GLM-4.7 scored 41%; MiMo-V2.5-Pro scored 68.4%. MiMo-V2.5-Pro wins this benchmark. - Source ↗
HLE
KnowledgeA 24.8%B 48%Winner: MiMo-V2.5-ProΔ 23.2HLE: GLM-4.7 scored 24.8%; MiMo-V2.5-Pro scored 48%. 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-4.7 | MiMo-V2.5-Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | MiMo-V2.5-ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | MiMo-V2.5-ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | MiMo-V2.5-ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | MiMo-V2.5-Pro1M | MiMo-V2.5-Pro lists the larger context window. |
Benchmark Deep Dive
AgenticMiMo-V2.5-Pro wins16 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 68.4% | MiMo-V2.5-Pro leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 29.1% | MiMo-V2.5-Pro leads |
| τ²-bench resultsSource | 95.9% | 94.2% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | 62.70% | MiMo-V2.5-Pro leads |
| GDPval-AASource | 33.3% | 38.3% | MiMo-V2.5-Pro leads |
| GDPval-AASource | 1165 | 1265 | MiMo-V2.5-Pro leads |
| Claw-EvalSource | — | 63.8% | Not comparable |
| τ³-bench resultsSource | — | 72.9% | Not comparable |
| APEX-Agents-AASource | — | 2.4% | 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-4.7 wins8 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | 60.2% | MiMo-V2.5-Pro leads |
| AA-SciCodeSource | 45.1% | 50.2% | MiMo-V2.5-Pro leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SWE-bench ProSource | — | 57.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 68.4% | Not comparable |
Reasoning2 benchmarks
KnowledgeGLM-4.7 wins11 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | 48% | MiMo-V2.5-Pro leads |
| Artificial Analysis Intelligence IndexSource | 33.7% | 42.2% | MiMo-V2.5-Pro leads |
| AA-GPQA DiamondSource | 85.9% | 86.6% | MiMo-V2.5-Pro leads |
| AA-HLESource | 25.1% | 33.8% | MiMo-V2.5-Pro leads |
| AA-Omniscience IndexSource | -34.6% | 3.6% | MiMo-V2.5-Pro leads |
| AA-Omniscience AccuracySource | 29.3% | 22.6% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 24.5% | MiMo-V2.5-Pro leads |
| HLE w/o toolsSource | — | 34% | Not comparable |
| AA Openness IndexSource | — | 38.9% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | 1298 | MiMo-V2.5-Pro leads |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 79.9% | MiMo-V2.5-Pro leads |
Frequently Asked Questions (4)
Which is better, GLM-4.7 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is ahead on BenchLM's BenchAlign leaderboard, 70.19 to 61.16. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 68.4%.
Which is better for knowledge tasks, GLM-4.7 or MiMo-V2.5-Pro?
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 51.8 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-4.7 or MiMo-V2.5-Pro?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 57.2. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro has the edge for agentic tasks in this comparison, averaging 68.4 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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