Model comparison
DeepSeek V4 Pro vs MiniMax M2.7
Head-to-head evidence from 8 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro #46 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro and MiniMax M2.7 share 8 comparable benchmark results. 2 of 8 categories are comparable. 15 results are unique to DeepSeek V4 Pro; 27 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 8
- DeepSeek V4 Pro only
- 15
- MiniMax M2.7 only
- 27
- Comparable categories
- 2 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 8 shared benchmark results across 4 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
MiniMax M2.7 is clearly ahead on the BenchAlign aggregate, 64.11 to 60.66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro. DeepSeek V4 Pro gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.
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 V4 Pro | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V4 Pro65.3 | Margin← 12.0 | MiniMax M2.753.3 |
| Agentic | DeepSeek V4 Pro59.1 | Margin← 2.1 | MiniMax M2.757.0 |
| Knowledge | DeepSeek V4 Pro41.3 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V4 Pro31.7 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 52.1%B 56.2%Winner: MiniMax M2.7Δ 4.1SWE-bench Pro: DeepSeek V4 Pro scored 52.1%; MiniMax M2.7 scored 56.2%. MiniMax M2.7 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 59.1%B 57%Winner: DeepSeek V4 ProΔ 2.1Terminal-Bench 2.0: DeepSeek V4 Pro scored 59.1%; MiniMax M2.7 scored 57%. DeepSeek V4 Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro$0.435 input / $0.87 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V4 Pro has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 ProNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 ProNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro1M | MiniMax M2.7200K | DeepSeek V4 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro wins13 benchmarks
| Benchmark | DeepSeek V4 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | 57% | DeepSeek V4 Pro leads |
| MCP AtlasSource | 69.4% | — | Not comparable |
| ToolathlonSource | 46.3% | 46.3% | Tie |
| Claw-EvalSource | 59.8% | 48.7% | DeepSeek V4 Pro leads |
| Gert LabsSource | 50.28% | 40.40% | DeepSeek V4 Pro leads |
| ResearchClawBenchSource | 17.1% | — | Not comparable |
| τ²-bench resultsSource | — | 84.8% | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| AA Agentic IndexSource | — | 25.6% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| GDPval-AASource | — | 32.9% | Not comparable |
| GDPval-AASource | — | 1158 | Not comparable |
CodingDeepSeek V4 Pro wins13 benchmarks
| Benchmark | DeepSeek V4 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.6% | — | Not comparable |
| SWE-bench ProSource | 52.1% | 56.2% | MiniMax M2.7 leads |
| SWE MultilingualSource | 69.8% | 76.5% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| NL2RepoSource | — | 39.8% | Not comparable |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
| AA Coding IndexSource | — | 52.6% | Not comparable |
| AA-SciCodeSource | — | 47.0% | Not comparable |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | DeepSeek V4 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 82.9% | — | Not comparable |
| SimpleQASource | 45% | — | Not comparable |
| Chinese-SimpleQASource | 75.8% | — | Not comparable |
| GPQASource | 72.9% | — | Not comparable |
| GPQA-DSource | 72.9% | 87.0% | MiniMax M2.7 leads |
| HLESource | 7.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 38.1% | Not comparable |
| AA-GPQA DiamondSource | — | 87.4% | Not comparable |
| AA-HLESource | — | 28.1% | Not comparable |
| AA-Omniscience IndexSource | — | 0.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.4% | Not comparable |
Math5 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1264 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.7% | Not comparable |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Pro or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 60.66. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 52.1% and 56.2%.
Which is better for coding, DeepSeek V4 Pro or MiniMax M2.7?
DeepSeek V4 Pro has the edge for coding in this comparison, averaging 65.3 versus 53.3. Inside this category, SWE Multilingual is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro or MiniMax M2.7?
DeepSeek V4 Pro has the edge for agentic tasks in this comparison, averaging 59.1 versus 57. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
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