Model comparison
Kimi K2.5 vs Mellum2-12B-A2.5B-Thinking
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: Kimi K2.5 #54 (Supported); Mellum2-12B-A2.5B-Thinking unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.5 and Mellum2-12B-A2.5B-Thinking share 3 comparable benchmark results. 2 of 8 categories are comparable. 60 results are unique to Kimi K2.5; 2 to Mellum2-12B-A2.5B-Thinking.
Updated July 23, 2026- Shared results
- 3
- Kimi K2.5 only
- 60
- Mellum2-12B-A2.5B-Thinking only
- 2
- Comparable categories
- 2 / 8
Treat this as a split decision. Kimi K2.5 makes more sense if instruction following is the priority or you need the larger 256K context window; Mellum2-12B-A2.5B-Thinking is the better fit if knowledge is the priority or you want the stronger reasoning-first profile.
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
Kimi K2.5 and Mellum2-12B-A2.5B-Thinking 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.
Mellum2-12B-A2.5B-Thinking is the reasoning model in the pair, while Kimi K2.5 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. Kimi K2.5 gives you the larger context window at 256K, compared with 128K for Mellum2-12B-A2.5B-Thinking.
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 | Kimi K2.5 | Δ | Mellum2-12B-A2.5B-Thinking |
|---|---|---|---|
| Inst. Following | Kimi K2.593.9 | Margin← 17.4 | Mellum2-12B-A2.5B-Thinking76.5 |
| Knowledge | Kimi K2.556.9 | Margin→ 0.7 | Mellum2-12B-A2.5B-Thinking57.6 |
| Agentic | Kimi K2.555.0 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Coding | Kimi K2.559.4 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Reasoning | Kimi K2.561.0 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Math | Kimi K2.560.6 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Multilingual | Kimi K2.582.3 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Multimodal | Kimi K2.578.5 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 87.6%B 57.6%Winner: Kimi K2.5Δ 30GPQA: Kimi K2.5 scored 87.6%; Mellum2-12B-A2.5B-Thinking scored 57.6%. Kimi K2.5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 93.9%B 76.5%Winner: Kimi K2.5Δ 17.4IFEval: Kimi K2.5 scored 93.9%; Mellum2-12B-A2.5B-Thinking scored 76.5%. Kimi K2.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.5 | Mellum2-12B-A2.5B-Thinking | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.5$0.6 input / $3 output | Mellum2-12B-A2.5B-ThinkingNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Kimi K2.545 tok/s | Mellum2-12B-A2.5B-ThinkingNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.52.38 s | Mellum2-12B-A2.5B-ThinkingNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.5256K | Mellum2-12B-A2.5B-Thinking128K | Kimi K2.5 lists the larger context window. |
Benchmark Deep Dive
Agentic20 benchmarks
| Benchmark | Kimi K2.5 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 50.8% | — | Not comparable |
| BrowseCompSource | 60.6% | — | Not comparable |
| Claw-EvalSource | 52.3% | — | Not comparable |
| QwenClawBenchSource | 54.3% | — | Not comparable |
| τ³-bench resultsSource | 65.7% | — | Not comparable |
| DeepSearchQASource | 77.1% | — | Not comparable |
| DeepPlanningSource | 14.4% | — | Not comparable |
| ToolathlonSource | 27.8% | — | Not comparable |
| MCP AtlasSource | 29.5% | — | Not comparable |
| MCP-TasksSource | 59.1% | — | Not comparable |
| WideResearchSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| APEX-Agents-AASource | 11.5% | — | Not comparable |
| Gert LabsSource | 45.88% | — | Not comparable |
| ResearchClawBenchSource | 14.0% | — | Not comparable |
| JobBenchSource | 8.7% | — | Not comparable |
| AA Agentic IndexSource | 21.7% | — | Not comparable |
| GDPval-AASource | 25.4% | — | Not comparable |
| GDPval-AASource | 1009 | — | Not comparable |
| BFCL v4Source | — | 45.6% | Not comparable |
Coding10 benchmarks
| Benchmark | Kimi K2.5 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 76.8% | — | Not comparable |
| SWE-bench Verified*Source | 70.8% | — | Not comparable |
| LiveCodeBench v6Source | 85.0% | — | Not comparable |
| SWE-bench ProSource | 50.7% | — | Not comparable |
| SWE MultilingualSource | 73% | — | Not comparable |
| SWE-RebenchSource | 58.5% | — | Not comparable |
| React Native EvalsSource | 77.2% | — | Not comparable |
| SciCodeSource | 48.7% | — | Not comparable |
| AA-SciCodeSource | 49.0% | — | Not comparable |
| AA Coding IndexSource | 46.8% | — | Not comparable |
Reasoning3 benchmarks
KnowledgeMellum2-12B-A2.5B-Thinking wins13 benchmarks
| Benchmark | Kimi K2.5 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| GPQASource | 87.6% | 57.6% | Kimi K2.5 leads |
| GPQA-DSource | 87.6% | 57.6% | Kimi K2.5 leads |
| SuperGPQASource | 69.2% | — | Not comparable |
| MMLU-ProSource | 87.1% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 87.1% | — | Not comparable |
| HLESource | 30.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 35.4% | — | Not comparable |
| AA-GPQA DiamondSource | 87.9% | — | Not comparable |
| AA-HLESource | 29.4% | — | Not comparable |
| AA-Omniscience IndexSource | -8.1% | — | Not comparable |
| AA-Omniscience AccuracySource | 34.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 64.6% | — | Not comparable |
| MMLU-ReduxSource | — | 86.2% | Not comparable |
Math9 benchmarks
| Benchmark | Kimi K2.5 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| AIME 2025Source | 96.1% | — | Not comparable |
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 96.3% | — | Not comparable |
| HMMT Feb 2025Source | 95.4% | — | Not comparable |
| HMMT Nov 2025Source | 91.1% | — | Not comparable |
| HMMT Feb 2026Source | 87.1% | — | Not comparable |
| MMAnswerBenchSource | 81.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 27.900% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 4.200% | — | Not comparable |
Multilingual2 benchmarks
Multimodal6 benchmarks
Frequently Asked Questions (3)
Which is better, Kimi K2.5 or Mellum2-12B-A2.5B-Thinking?
Kimi K2.5 and Mellum2-12B-A2.5B-Thinking 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, Kimi K2.5 or Mellum2-12B-A2.5B-Thinking?
Mellum2-12B-A2.5B-Thinking has the edge for knowledge tasks in this comparison, averaging 57.6 versus 56.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for instruction following, Kimi K2.5 or Mellum2-12B-A2.5B-Thinking?
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 76.5. Inside this category, IFEval 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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