Model comparison
GLM-4.7 vs MiMo-V2.5
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); MiMo-V2.5 #62 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and MiMo-V2.5 share 3 comparable benchmark results. 2 of 8 categories are comparable. 27 results are unique to GLM-4.7; 8 to MiMo-V2.5.
Updated July 21, 2026- Shared results
- 3
- GLM-4.7 only
- 27
- MiMo-V2.5 only
- 8
- Comparable categories
- 2 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-4.7 has the cleaner BenchAlign overall profile here, landing at 61.16 versus 58.62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 56.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 65.8%. MiMo-V2.5 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5 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 |
|---|---|---|---|
| Agentic | GLM-4.745.7 | Margin→ 20.1 | MiMo-V2.565.8 |
| Coding | GLM-4.775.4 | Margin← 19.3 | MiMo-V2.556.1 |
| Knowledge | GLM-4.751.8 | MarginNo overlap | MiMo-V2.5Not measured |
| Math | GLM-4.71.8 | MarginNo overlap | MiMo-V2.5Not measured |
| Multimodal | GLM-4.7Not measured | MarginNo overlap | MiMo-V2.579.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 65.8%Winner: MiMo-V2.5Δ 24.8Terminal-Bench 2.0: GLM-4.7 scored 41%; MiMo-V2.5 scored 65.8%. MiMo-V2.5 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 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | MiMo-V2.5Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | MiMo-V2.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | MiMo-V2.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | MiMo-V2.51M | MiMo-V2.5 lists the larger context window. |
Benchmark Deep Dive
AgenticMiMo-V2.5 wins11 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 65.8% | MiMo-V2.5 leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | 46.89% | MiMo-V2.5 leads |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
| Claw-EvalSource | — | 62.3% | Not comparable |
| MM-ClawBenchSource | — | 23.8% | Not comparable |
| ResearchClawBenchSource | — | 16.9% | Not comparable |
CodingGLM-4.7 wins8 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| AA-SciCodeSource | 45.1% | — | Not comparable |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SWE-bench ProSource | — | 56.1% | Not comparable |
| Terminal-Bench 2.0Source | — | 65.8% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5 | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.9% | — | Not comparable |
| AA-HLESource | 25.1% | — | Not comparable |
| AA-Omniscience IndexSource | -34.6% | — | Not comparable |
| AA-Omniscience AccuracySource | 29.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.3% | — | Not comparable |
Math3 benchmarks
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-4.7 or MiMo-V2.5?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 58.62. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 65.8%.
Which is better for coding, GLM-4.7 or MiMo-V2.5?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 56.1. MiMo-V2.5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-4.7 or MiMo-V2.5?
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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