Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs MiniMax-M2.7
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 26.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
MiniMaxSummary
- The widest gap is in knowledge, where MiniMax-M2.7 leads 37.7 to 16.0.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 | |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Noometry Index | 26.1 | 37.7 |
| Released | 2025-01-20 | 2026-03-18 |
| Weights | Open | Open |
| Context window | — | 205K |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.30 |
| Output $ / M tokens | — | $1.20 |
| Results tracked | 5 | 30 |
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Category by category
Coding MiniMax-M2.7 leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), MiniMax-M2.7: 41.8 (#120)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| LMArena WebDev | — | 1398 |
| SciCode | — | 47% |
| WeirdML | — | 37% |
| BigCodeBench Instruct | 7% | — |
| LMArena Coding | — | 1454 |
| BigCodeBench Complete | 7.9% | — |
| ALE-Bench | — | 599.25 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, MiniMax-M2.7: 25.1 (#111)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | — | 45.1% |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |
Reasoning Too close to call
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), MiniMax-M2.7: 19.7 (#253)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| NYT Connections (extended) | — | 24.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 39.3% |
| LMArena Hard Prompts | — | 1422 |
| Epoch Capabilities Index | — | 145.85 |
Math MiniMax-M2.7 leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), MiniMax-M2.7: 25.9 (#263)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | — |
| ProofBench | — | 3% |
| LMArena Math | — | 1420 |
Knowledge MiniMax-M2.7 leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), MiniMax-M2.7: 37.7 (#152)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| GPQA Diamond | 33.6% | — |
| Vectara Hallucination Rate | — | 12.9% |
| LMArena Expert | — | 1444 |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, MiniMax-M2.7: 50.3 (#123)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | — | 1382 |
| LMArena Chinese | — | 1441 |
| LMArena French | — | 1421 |
| LMArena German | — | 1398 |
| LMArena Japanese | — | 1262 |
| LMArena Korean | — | 1313 |
| LMArena Russian | — | 1383 |
| LMArena Spanish | — | 1403 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, MiniMax-M2.7: 74.1 (#103)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | — | 1405 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, MiniMax-M2.7: 43.3 (#99)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | — | 1419 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, MiniMax-M2.7: 58.9 (#112)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | — | 1405 |
| LMArena Creative Writing | — | 1354 |
| LMArena Multi-Turn | — | 1412 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than MiniMax-M2.7?
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or MiniMax-M2.7 better for coding?
MiniMax-M2.7 scores higher on coding benchmarks: 41.8 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and MiniMax-M2.7 share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and MiniMax-M2.7 has 30.