# DeepSeek V3.2 vs MiniMax M2.7

> Side-by-side benchmark comparison for DeepSeek V3.2 and MiniMax M2.7 across agentic, coding, multimodal, knowledge, reasoning, multilingual, and math tasks.

- Canonical page: https://benchlm.ai/compare/deepseek-v3-2-vs-minimax-m2-7
- Last updated: September 15, 2026

- Shared sourced benchmarks: 4
- HTML indexing: indexable
- Ranking lane: BenchAlign v5

## Quick Verdict

Pick DeepSeek V3.2 if you want the stronger benchmark profile. MiniMax M2.7 only makes more sense when its price, context window, or workload-specific category wins matter more than the overall score.

## Summary

- DeepSeek V3.2 leads overall 56.87 to 55.14.
- The clearest category separation is in instruction following, where the averages are 58.3 for DeepSeek V3.2 and 93 for MiniMax M2.7.
- The biggest single benchmark swing is Gert Labs in Agentic, with scores of 29.57% and 40.40%.
- DeepSeek V3.2 is the cheaper option on output tokens, which matters if you expect large responses or heavy interactive use.
- MiniMax M2.7 also has the larger context window at 200K.

## Model Snapshot

| Property | DeepSeek V3.2 | MiniMax M2.7 |
|----------|----------|----------|
| Creator | DeepSeek | MiniMax |
| Type | Open Weight | Open Weight |
| Reasoning | Non-Reasoning | Non-Reasoning |
| Context | 128K | 200K |
| Overall Score | 56.87 | 55.14 |
| Benchmarks Covered | 7 | 23 |
| Pricing (input/output) | $0.28 / $0.42 | $0.30 / $1.20 |

## Category Breakdown

### Agentic

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: Coming soon
- MiniMax M2.7 public-lane score: 41.1 (Estimated · #110/153)

| Benchmark | DeepSeek V3.2 | MiniMax M2.7 | Winner |
|-----------|-----------|-----------|--------|
| Claw-Eval | 40.2% | 48.7% | MiniMax M2.7 |
| VITA-Bench | 18.5% | Coming soon | Coming soon |
| Gert Labs | 29.57% | 40.40% | MiniMax M2.7 |
| Terminal-Bench 2.0 | Coming soon | 57% | Coming soon |
| Toolathlon | Coming soon | 46.3% | Coming soon |
| MLE-Bench Lite | Coming soon | 66.6% | Coming soon |
| MM-ClawBench | Coming soon | 62.7% | Coming soon |
| Terminal-Bench 2.1 (Vals) | Coming soon | 48.7% | Coming soon |

### Coding

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: 50 (Estimated · #63/152)
- MiniMax M2.7 public-lane score: 48.6 (Estimated · #68/152)

| Benchmark | DeepSeek V3.2 | MiniMax M2.7 | Winner |
|-----------|-----------|-----------|--------|
| SWE-Rebench | 60.9% | 51.9% | DeepSeek V3.2 |
| React Native Evals | 71.5% | 71.4% | DeepSeek V3.2 |
| SWE-bench Verified* | Coming soon | 75.4% | Coming soon |
| SWE-bench Pro | Coming soon | 56.2% | Coming soon |
| SWE Multilingual | Coming soon | 76.5% | Coming soon |
| Multi-SWE Bench | Coming soon | 52.7% | Coming soon |
| VIBE-Pro | Coming soon | 55.6% | Coming soon |
| NL2Repo | Coming soon | 39.8% | Coming soon |
| Vibe Code Bench | Coming soon | 27.04% | Coming soon |
| LiveCodeBench (Vals) | Coming soon | 79.9% | Coming soon |
| SWE-bench (Vals) | Coming soon | 73.8% | Coming soon |

### Reasoning

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: 52.4 (Unranked · 2 rankable rows)
- MiniMax M2.7 public-lane score: 74.8 (Unranked · 2 rankable rows)

### Knowledge

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: 48.9 (Estimated · #89/183)
- MiniMax M2.7 public-lane score: 48.7 (Supported · #90/183)

| Benchmark | DeepSeek V3.2 | MiniMax M2.7 | Winner |
|-----------|-----------|-----------|--------|
| GPQA-D | Coming soon | 87.0% | Coming soon |
| MMLU-Pro (Arcee) | Coming soon | 80.8% | Coming soon |
| GPQA Diamond (Vals) | Coming soon | 86.6% | Coming soon |
| MMLU-Pro (Vals) | Coming soon | 80.4% | Coming soon |

### Instruction Following

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: 58.3 (#73/123)
- MiniMax M2.7 public-lane score: 93 (#10/123)

### Mathematics

- Winner: Coming soon
- DeepSeek V3.2 public-lane score: 40.2 (Unranked · 2 rankable rows)
- MiniMax M2.7 public-lane score: Coming soon

| Benchmark | DeepSeek V3.2 | MiniMax M2.7 | Winner |
|-----------|-----------|-----------|--------|
| FrontierMath v2 (Tiers 1-3) | 22.100% | Coming soon | Coming soon |
| FrontierMath v2 (Tier 4) | 2.100% | Coming soon | Coming soon |
| AIME25 (Arcee) | Coming soon | 80.0% | Coming soon |

## FAQ

### Which is better overall, DeepSeek V3.2 or MiniMax M2.7?

DeepSeek V3.2 is ahead overall on BenchLM right now.

### Where is the biggest gap between DeepSeek V3.2 and MiniMax M2.7?

The widest category gap is in instruction following, where the averages are 58.3 for DeepSeek V3.2 and 93 for MiniMax M2.7.

### How many benchmarks does DeepSeek V3.2 cover on BenchLM?

DeepSeek V3.2 currently has 7 sourced benchmark scores on BenchLM.

### How many benchmarks does MiniMax M2.7 cover on BenchLM?

MiniMax M2.7 currently has 23 sourced benchmark scores on BenchLM.

## Related Comparisons

- [DeepSeek V3.2 vs DeepSeek V3.2 (Thinking)](/compare/deepseek-v3-2-vs-deepseek-v3-2-thinking)
- [MiniMax M2.7 vs DeepSeek V3.2 (Thinking)](/compare/deepseek-v3-2-thinking-vs-minimax-m2-7)
- [DeepSeek V3.2 vs Claude Fable 5.1](/compare/claude-fable-5-1-vs-deepseek-v3-2)
- [MiniMax M2.7 vs Claude Fable 5.1](/compare/claude-fable-5-1-vs-minimax-m2-7)

## Explore More

- [DeepSeek V3.2 profile](/models/deepseek-v3-2)
- [MiniMax M2.7 profile](/models/minimax-m2-7)
- [Compare Pricing](/llm-pricing)
- [Alternative Finder](/tools/alternative-finder)
- [LLM Selector](/tools/llm-selector)
- [Overall Rankings](/best/overall)