Model comparison
GLM-4.7 vs Mellum2-12B-A2.5B-Instruct
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Mellum2-12B-A2.5B-Instruct unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Mellum2-12B-A2.5B-Instruct share 1 comparable benchmark result. 1 of 8 categories are comparable. 29 results are unique to GLM-4.7; 4 to Mellum2-12B-A2.5B-Instruct.
Updated July 23, 2026- Shared results
- 1
- GLM-4.7 only
- 29
- Mellum2-12B-A2.5B-Instruct only
- 4
- Comparable categories
- 1 / 8
Treat this as a split decision. GLM-4.7 makes more sense if knowledge is the priority or you need the larger 200K context window; Mellum2-12B-A2.5B-Instruct is the better fit if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 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 and Mellum2-12B-A2.5B-Instruct finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GLM-4.7 is the reasoning model in the pair, while Mellum2-12B-A2.5B-Instruct 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. GLM-4.7 gives you the larger context window at 200K, compared with 128K for Mellum2-12B-A2.5B-Instruct.
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 | Mellum2-12B-A2.5B-Instruct |
|---|---|---|---|
| Knowledge | GLM-4.751.8 | Margin← 10.9 | Mellum2-12B-A2.5B-Instruct40.9 |
| Agentic | GLM-4.745.7 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Coding | GLM-4.775.4 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | Mellum2-12B-A2.5B-Instruct75.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 85.7%B 40.9%Winner: GLM-4.7Δ 44.8GPQA: GLM-4.7 scored 85.7%; Mellum2-12B-A2.5B-Instruct scored 40.9%. GLM-4.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Mellum2-12B-A2.5B-Instruct | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Mellum2-12B-A2.5B-InstructNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | Mellum2-12B-A2.5B-InstructNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Mellum2-12B-A2.5B-InstructNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Mellum2-12B-A2.5B-Instruct128K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | GLM-4.7 | Mellum2-12B-A2.5B-Instruct | 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% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
| BFCL v4Source | — | 44.2% | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
KnowledgeGLM-4.7 wins11 benchmarks
| Benchmark | GLM-4.7 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| GPQASource | 85.7% | 40.9% | GLM-4.7 leads |
| 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 |
| MMLU-ReduxSource | — | 78.1% | Not comparable |
| GPQA-DSource | — | 40.9% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-4.7 or Mellum2-12B-A2.5B-Instruct?
GLM-4.7 and Mellum2-12B-A2.5B-Instruct are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, GLM-4.7 or Mellum2-12B-A2.5B-Instruct?
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 51.8 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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