Model comparison
DeepSeek V4 Pro vs MiniMax-M2
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 37.4 on the Noometry Index. MiniMax-M2 costs 1.9× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
DeepSeek
MiniMaxSummary
- They share 19 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 9 categories and MiniMax-M2 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 19.4.
- The biggest single-benchmark swing is NYT Connections (extended): 91.3% for DeepSeek V4 Pro and 14.8% for MiniMax-M2.
- MiniMax-M2 is cheaper at $0.30 / $1.20 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 205K.
Side by side
| DeepSeek V4 Pro | MiniMax-M2 | |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Noometry Index | 54.3 | 37.4 |
| Released | 2026-04-24 | 2025-10-27 |
| Weights | Open | Open |
| Context window | 1M | 205K |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $0.30 |
| Output $ / M tokens | $1.98 | $1.20 |
| Results tracked | 48 | 21 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), MiniMax-M2: 39.3 (#159)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena WebDev | 1582 | 1297 |
| LMArena Coding | 1470 | 1370 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| SWE-bench Verified (bash only) | — | 61% |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| ALE-Bench | 1,403 | — |
Agentic & Tool Use DeepSeek V4 Pro leads
DeepSeek V4 Pro: 32.8 (#58), MiniMax-M2: 25.1 (#109)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| Vending-Bench 2 | 3,285 | 160.6 |
| Terminal-Bench | — | 30% |
| APEX-Agents | 47.3% | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), MiniMax-M2: 19.4 (#258)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| Kagi LLM Benchmark | 53.5% | 57.8% |
| NYT Connections (extended) | 91.3% | 14.8% |
| LMArena Hard Prompts | 1461 | 1357 |
| ARC-AGI-2 | 61.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Epoch Capabilities Index | 155.31 | — |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), MiniMax-M2: 37.3 (#160)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Math | 1455 | 1352 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), MiniMax-M2: 37.0 (#163)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Expert | 1464 | 1337 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), MiniMax-M2: 45.3 (#171)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Non-English | 1439 | 1313 |
| LMArena Chinese | 1486 | 1366 |
| LMArena French | 1472 | 1335 |
| LMArena German | 1458 | 1355 |
| LMArena Russian | 1453 | 1331 |
| LMArena Spanish | 1458 | 1326 |
| LMArena Japanese | 1445 | — |
| LMArena Korean | 1447 | — |
Instruction Following DeepSeek V4 Pro leads
DeepSeek V4 Pro: 76.1 (#47), MiniMax-M2: 70.2 (#166)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Instruction Following | 1448 | 1328 |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), MiniMax-M2: 40.5 (#153)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Longer Query | 1458 | 1331 |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), MiniMax-M2: 53.0 (#162)
| Benchmark | DeepSeek V4 Pro | MiniMax-M2 |
|---|---|---|
| LMArena Text | 1451 | 1340 |
| LMArena Creative Writing | 1446 | 1286 |
| LMArena Multi-Turn | 1467 | 1361 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than MiniMax-M2?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 37.4 on the Noometry Index. MiniMax-M2 costs 1.9× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro or MiniMax-M2?
MiniMax-M2 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or MiniMax-M2 better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 39.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 205K.
How many benchmarks do DeepSeek V4 Pro and MiniMax-M2 share?
19 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and MiniMax-M2 has 21.