Model comparison
GLM-5 vs Mellum2-12B-A2.5B-Instruct
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-5 #28 (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 and Mellum2-12B-A2.5B-Instruct share 3 comparable benchmark results. 2 of 8 categories are comparable. 46 results are unique to GLM-5; 2 to Mellum2-12B-A2.5B-Instruct.
Updated July 23, 2026- Shared results
- 3
- GLM-5 only
- 46
- Mellum2-12B-A2.5B-Instruct only
- 2
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-5 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 its strengths line up with your actual workload.
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-5 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 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-5 | Δ | Mellum2-12B-A2.5B-Instruct |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin← 25.5 | Mellum2-12B-A2.5B-Instruct40.9 |
| Inst. Following | GLM-592.6 | Margin← 16.8 | Mellum2-12B-A2.5B-Instruct75.8 |
| Agentic | GLM-556.2 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Math | GLM-556.3 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Mellum2-12B-A2.5B-InstructNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 40.9%Winner: GLM-5Δ 45.1GPQA: GLM-5 scored 86%; Mellum2-12B-A2.5B-Instruct scored 40.9%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 75.8%Winner: GLM-5Δ 16.8IFEval: GLM-5 scored 92.6%; Mellum2-12B-A2.5B-Instruct scored 75.8%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Mellum2-12B-A2.5B-Instruct | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Mellum2-12B-A2.5B-InstructNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | Mellum2-12B-A2.5B-InstructNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Mellum2-12B-A2.5B-InstructNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Mellum2-12B-A2.5B-Instruct128K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | GLM-5 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | — | Not comparable |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| BFCL v4Source | — | 44.2% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | — | Not comparable |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins13 benchmarks
| Benchmark | GLM-5 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| GPQASource | 86% | 40.9% | GLM-5 leads |
| GPQA-DSource | 86.0% | 40.9% | GLM-5 leads |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
| MMLU-ReduxSource | — | 78.1% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | Mellum2-12B-A2.5B-Instruct | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5 or Mellum2-12B-A2.5B-Instruct?
GLM-5 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 or Mellum2-12B-A2.5B-Instruct?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or Mellum2-12B-A2.5B-Instruct?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 75.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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