Model comparison
Mellum2-12B-A2.5B-Instruct vs Qwen3.5-27B
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Mellum2-12B-A2.5B-Instruct unranked (Not scored); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Mellum2-12B-A2.5B-Instruct and Qwen3.5-27B share 2 comparable benchmark results. 2 of 8 categories are comparable. 3 results are unique to Mellum2-12B-A2.5B-Instruct; 26 to Qwen3.5-27B.
Updated July 23, 2026- Shared results
- 2
- Mellum2-12B-A2.5B-Instruct only
- 3
- Qwen3.5-27B only
- 26
- Comparable categories
- 2 / 8
Treat this as a split decision. Mellum2-12B-A2.5B-Instruct makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-27B is the better fit if knowledge is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 2 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
Mellum2-12B-A2.5B-Instruct and Qwen3.5-27B 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.
Qwen3.5-27B 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. Qwen3.5-27B gives you the larger context window at 262K, 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 | Mellum2-12B-A2.5B-Instruct | Δ | Qwen3.5-27B |
|---|---|---|---|
| Knowledge | Mellum2-12B-A2.5B-Instruct40.9 | Margin→ 41.8 | Qwen3.5-27B82.7 |
| Inst. Following | Mellum2-12B-A2.5B-Instruct75.8 | Margin→ 19.2 | Qwen3.5-27B95.0 |
| Agentic | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | Qwen3.5-27B52.0 |
| Coding | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | Qwen3.5-27B64.9 |
| Reasoning | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Multilingual | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | Qwen3.5-27B82.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 40.9%B 85.5%Winner: Qwen3.5-27BΔ 44.6GPQA: Mellum2-12B-A2.5B-Instruct scored 40.9%; Qwen3.5-27B scored 85.5%. Qwen3.5-27B wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 75.8%B 95%Winner: Qwen3.5-27BΔ 19.2IFEval: Mellum2-12B-A2.5B-Instruct scored 75.8%; Qwen3.5-27B scored 95%. Qwen3.5-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mellum2-12B-A2.5B-Instruct | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mellum2-12B-A2.5B-InstructNot available | Qwen3.5-27B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | Mellum2-12B-A2.5B-InstructNot available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mellum2-12B-A2.5B-InstructNot available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mellum2-12B-A2.5B-Instruct128K | Qwen3.5-27B262K | Qwen3.5-27B lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
Coding3 benchmarks
Reasoning3 benchmarks
KnowledgeQwen3.5-27B wins11 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ReduxSource | 78.1% | — | Not comparable |
| GPQASource | 40.9% | 85.5% | Qwen3.5-27B leads |
| GPQA-DSource | 40.9% | — | Not comparable |
| MMLU-ProSource | — | 86.1% | Not comparable |
| SuperGPQASource | — | 65.6% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.8% | Not comparable |
| AA-GPQA DiamondSource | — | 85.8% | Not comparable |
| AA-HLESource | — | 22.2% | Not comparable |
| AA-Omniscience IndexSource | — | -42.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 21.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 79.7% | Not comparable |
Multilingual1 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (3)
Which is better, Mellum2-12B-A2.5B-Instruct or Qwen3.5-27B?
Mellum2-12B-A2.5B-Instruct and Qwen3.5-27B 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, Mellum2-12B-A2.5B-Instruct or Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for instruction following, Mellum2-12B-A2.5B-Instruct or Qwen3.5-27B?
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 75.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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