Model comparison
GLM-5.1 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-5.1 #18 (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-5.1 and Mellum2-12B-A2.5B-Instruct share 1 comparable benchmark result. 1 of 8 categories are comparable. 35 results are unique to GLM-5.1; 4 to Mellum2-12B-A2.5B-Instruct.
Updated July 23, 2026- Shared results
- 1
- GLM-5.1 only
- 35
- Mellum2-12B-A2.5B-Instruct only
- 4
- Comparable categories
- 1 / 8
Treat this as a split decision. GLM-5.1 makes more sense if knowledge is the priority or you need the larger 203K 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-5.1 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-5.1 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-5.1 gives you the larger context window at 203K, 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-5.1 | Δ | Mellum2-12B-A2.5B-Instruct |
|---|---|---|---|
| Knowledge | GLM-5.152.3 | Margin← 11.4 | Mellum2-12B-A2.5B-Instruct40.9 |
| Agentic | GLM-5.165.4 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Coding | GLM-5.161.3 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Math | GLM-5.162.0 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Inst. Following | GLM-5.1Not measured | MarginNo overlap | Mellum2-12B-A2.5B-Instruct75.8 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | Mellum2-12B-A2.5B-Instruct | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Mellum2-12B-A2.5B-InstructNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-5.1Not available | Mellum2-12B-A2.5B-InstructNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Mellum2-12B-A2.5B-InstructNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Mellum2-12B-A2.5B-Instruct128K | GLM-5.1 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5.1 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | — | Not comparable |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | — | Not comparable |
| τ²-bench resultsSource | 97.7% | — | Not comparable |
| GDPval-AASource | 37.8% | — | Not comparable |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | — | Not comparable |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| BFCL v4Source | — | 44.2% | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
KnowledgeGLM-5.1 wins10 benchmarks
| Benchmark | GLM-5.1 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | 40.9% | GLM-5.1 leads |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | — | Not comparable |
| AA-GPQA DiamondSource | 86.8% | — | Not comparable |
| AA-HLESource | 28.0% | — | Not comparable |
| AA-Omniscience IndexSource | 1.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 24.2% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 29.4% | — | Not comparable |
| MMLU-ReduxSource | — | 78.1% | Not comparable |
| GPQASource | — | 40.9% | Not comparable |
Math6 benchmarks
| Benchmark | GLM-5.1 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| AIME26Source | 95.3% | — | Not comparable |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | — | Not comparable |
| MMAnswerBenchSource | 83.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 12.500% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | GLM-5.1 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-5.1 or Mellum2-12B-A2.5B-Instruct?
GLM-5.1 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-5.1 or Mellum2-12B-A2.5B-Instruct?
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 40.9. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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