Model comparison
Mellum2-12B-A2.5B-Instruct vs MiniMax M2.7
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: Mellum2-12B-A2.5B-Instruct unranked (Not scored); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Mellum2-12B-A2.5B-Instruct and MiniMax M2.7 share 1 comparable benchmark result. 0 of 8 categories are comparable. 4 results are unique to Mellum2-12B-A2.5B-Instruct; 34 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 1
- Mellum2-12B-A2.5B-Instruct only
- 4
- MiniMax M2.7 only
- 34
- Comparable categories
- 0 / 8
Benchmark data for Mellum2-12B-A2.5B-Instruct and MiniMax M2.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
MiniMax M2.7 has 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 | Mellum2-12B-A2.5B-Instruct | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | MiniMax M2.757.0 |
| Coding | Mellum2-12B-A2.5B-InstructNot measured | MarginNo overlap | MiniMax M2.753.3 |
| Knowledge | Mellum2-12B-A2.5B-Instruct40.9 | MarginNo overlap | MiniMax M2.7Not measured |
| Inst. Following | Mellum2-12B-A2.5B-Instruct75.8 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mellum2-12B-A2.5B-InstructNot available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | Mellum2-12B-A2.5B-InstructNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mellum2-12B-A2.5B-InstructNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mellum2-12B-A2.5B-Instruct128K | MiniMax M2.7200K | MiniMax M2.7 lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Result |
|---|---|---|---|
| BFCL v4Source | 44.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 57% | Not comparable |
| τ²-bench resultsSource | — | 84.8% | Not comparable |
| ToolathlonSource | — | 46.3% | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| AA Agentic IndexSource | — | 25.6% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| GDPval-AASource | — | 32.9% | Not comparable |
| GDPval-AASource | — | 1158 | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
Coding11 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-bench ProSource | — | 56.2% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| SWE MultilingualSource | — | 76.5% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| NL2RepoSource | — | 39.8% | Not comparable |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
| AA Coding IndexSource | — | 52.6% | Not comparable |
| AA-SciCodeSource | — | 47.0% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ReduxSource | 78.1% | — | Not comparable |
| GPQASource | 40.9% | — | Not comparable |
| GPQA-DSource | 40.9% | 87.0% | MiniMax M2.7 leads |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 38.1% | Not comparable |
| AA-GPQA DiamondSource | — | 87.4% | Not comparable |
| AA-HLESource | — | 28.1% | Not comparable |
| AA-Omniscience IndexSource | — | 0.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.4% | Not comparable |
Math1 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Mellum2-12B-A2.5B-Instruct | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Frequently Asked Questions (3)
Can I compare Mellum2-12B-A2.5B-Instruct and MiniMax M2.7 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Mellum2-12B-A2.5B-Instruct and MiniMax M2.7 today?
MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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