Model comparison
DeepSeek V3 vs Mellum2-12B-A2.5B-Thinking
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: DeepSeek V3 #147 (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. DeepSeek V3 and Mellum2-12B-A2.5B-Thinking share 2 comparable benchmark results. 2 of 8 categories are comparable. 20 results are unique to DeepSeek V3; 3 to Mellum2-12B-A2.5B-Thinking.
Updated July 23, 2026- Shared results
- 2
- DeepSeek V3 only
- 20
- Mellum2-12B-A2.5B-Thinking only
- 3
- Comparable categories
- 2 / 8
Treat this as a split decision. DeepSeek V3 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Mellum2-12B-A2.5B-Thinking is the better fit if you want the stronger reasoning-first profile.
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
DeepSeek V3 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 DeepSeek V3 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.
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 | DeepSeek V3 | Δ | Mellum2-12B-A2.5B-Thinking |
|---|---|---|---|
| Knowledge | DeepSeek V372.7 | Margin← 15.1 | Mellum2-12B-A2.5B-Thinking57.6 |
| Inst. Following | DeepSeek V386.1 | Margin← 9.6 | Mellum2-12B-A2.5B-Thinking76.5 |
| Coding | DeepSeek V338.9 | MarginNo overlap | Mellum2-12B-A2.5B-ThinkingNot measured |
| Math | DeepSeek V31.7 | 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 ↗
IFEval
Inst. FollowingA 86.1%B 76.5%Winner: DeepSeek V3Δ 9.6IFEval: DeepSeek V3 scored 86.1%; Mellum2-12B-A2.5B-Thinking scored 76.5%. DeepSeek V3 wins this benchmark. - Source ↗
GPQA
KnowledgeA 59.1%B 57.6%Winner: DeepSeek V3Δ 1.5GPQA: DeepSeek V3 scored 59.1%; Mellum2-12B-A2.5B-Thinking scored 57.6%. DeepSeek V3 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | Mellum2-12B-A2.5B-Thinking | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | Mellum2-12B-A2.5B-ThinkingNot available | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V3Not available | Mellum2-12B-A2.5B-ThinkingNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | Mellum2-12B-A2.5B-ThinkingNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | Mellum2-12B-A2.5B-Thinking128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding4 benchmarks
Reasoning2 benchmarks
KnowledgeDeepSeek V3 wins10 benchmarks
| Benchmark | DeepSeek V3 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| GPQASource | 59.1% | 57.6% | DeepSeek V3 leads |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | — | Not comparable |
| AA-GPQA DiamondSource | 55.7% | — | Not comparable |
| AA-HLESource | 3.6% | — | Not comparable |
| AA-Omniscience IndexSource | -41.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.4% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 89.4% | — | Not comparable |
| MMLU-ReduxSource | — | 86.2% | Not comparable |
| GPQA-DSource | — | 57.6% | Not comparable |
Math1 benchmarks
| Benchmark | DeepSeek V3 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.724% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | Mellum2-12B-A2.5B-Thinking | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1150 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, DeepSeek V3 or Mellum2-12B-A2.5B-Thinking?
DeepSeek V3 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, DeepSeek V3 or Mellum2-12B-A2.5B-Thinking?
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 72.7 versus 57.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for instruction following, DeepSeek V3 or Mellum2-12B-A2.5B-Thinking?
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 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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