Model comparison
GLM-4.7 vs MiMo-V2-Pro
Head-to-head evidence from 14 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.7 #32; MiMo-V2-Pro unranked
BenchAlign evidence: GLM-4.7 supported; MiMo-V2-Pro supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and MiMo-V2-Pro share 14 comparable benchmark results. 1 of 8 categories are comparable. 17 results are unique to GLM-4.7; 2 to MiMo-V2-Pro.
Updated July 16, 2026- Shared results
- 14
- GLM-4.7 only
- 17
- MiMo-V2-Pro only
- 2
- Comparable categories
- 1 / 8
Pick MiMo-V2-Pro if you want the stronger benchmark profile. GLM-4.7 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 14 shared benchmark results across 5 evidence categories; 1 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-Pro is clearly ahead on the provisional aggregate, 72 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2-Pro's sharpest advantage is in coding, where it averages 78 against 75.4. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 78%.
MiMo-V2-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-Pro |
|---|---|---|---|
| Coding | GLM-4.775.4 | Margin→ 2.6 | MiMo-V2-Pro78.0 |
| Agentic | GLM-4.745.7 | MarginNo overlap | MiMo-V2-ProNot measured |
| Knowledge | GLM-4.752.1 | MarginNo overlap | MiMo-V2-ProNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | MiMo-V2-ProNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 73.8%B 78%Winner: MiMo-V2-ProΔ 4.2SWE-bench Verified: GLM-4.7 scored 73.8%; MiMo-V2-Pro scored 78%. MiMo-V2-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-Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | MiMo-V2-ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | MiMo-V2-ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | MiMo-V2-ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | MiMo-V2-Pro1M | MiMo-V2-Pro lists the larger context window. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2-Pro | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | 95% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | 36.68% | GLM-4.7 leads |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
| Claw-EvalSource | — | 57.8% | Not comparable |
| ResearchClawBenchSource | — | 15.3% | Not comparable |
CodingMiMo-V2-Pro wins7 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2-Pro | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 78% | MiMo-V2-Pro leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| Terminal-Bench HardSource | 31.8% | 40.9% | MiMo-V2-Pro leads |
| AA-SciCodeSource | 45.1% | 42.5% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2-Pro | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 40.3% | MiMo-V2-Pro leads |
| AA-GPQA DiamondSource | 85.9% | 87.0% | MiMo-V2-Pro leads |
| AA-HLESource | 25.1% | 28.3% | MiMo-V2-Pro leads |
| AA-Omniscience IndexSource | -34.6% | 4.9% | MiMo-V2-Pro leads |
| AA-Omniscience AccuracySource | 29.3% | 26.8% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 29.9% | MiMo-V2-Pro leads |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2-Pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | MiMo-V2-Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 68.8% | MiMo-V2-Pro leads |
Frequently Asked Questions (2)
Which is better, GLM-4.7 or MiMo-V2-Pro?
MiMo-V2-Pro is ahead on BenchLM's provisional leaderboard, 72 to 63. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 78%.
Which is better for coding, GLM-4.7 or MiMo-V2-Pro?
MiMo-V2-Pro has the edge for coding in this comparison, averaging 78 versus 75.4. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.