Model comparison
DeepSeek V4 Pro (Max) vs MiniMax M2.7
Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (Max) unranked (Not scored); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro (Max) and MiniMax M2.7 share 23 comparable benchmark results. 2 of 8 categories are comparable. 25 results are unique to DeepSeek V4 Pro (Max); 12 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 23
- DeepSeek V4 Pro (Max) only
- 25
- MiniMax M2.7 only
- 12
- Comparable categories
- 2 / 8
Treat this as a split decision. DeepSeek V4 Pro (Max) makes more sense if coding is the priority or you want the cheaper token bill; MiniMax M2.7 is the better fit if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 23 shared benchmark results across 6 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 V4 Pro (Max) and MiniMax M2.7 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.
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 (Max). DeepSeek V4 Pro (Max) is the reasoning model in the pair, while MiniMax M2.7 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. DeepSeek V4 Pro (Max) 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 (Max) | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V4 Pro (Max)70.9 | Margin← 17.6 | MiniMax M2.753.3 |
| Agentic | DeepSeek V4 Pro (Max)74.5 | Margin← 17.5 | MiniMax M2.757.0 |
| Knowledge | DeepSeek V4 Pro (Max)60.1 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V4 Pro (Max)95.2 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 67.9%B 57%Winner: DeepSeek V4 Pro (Max)Δ 10.9Terminal-Bench 2.0: DeepSeek V4 Pro (Max) scored 67.9%; MiniMax M2.7 scored 57%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.4%B 56.2%Winner: MiniMax M2.7Δ 0.8SWE-bench Pro: DeepSeek V4 Pro (Max) scored 55.4%; MiniMax M2.7 scored 56.2%. MiniMax M2.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (Max)Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (Max)Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (Max)1M | MiniMax M2.7200K | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro (Max) wins21 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 67.9% | 57% | DeepSeek V4 Pro (Max) leads |
| BrowseCompSource | 83.4% | — | Not comparable |
| HLE w/ toolsSource | 48.2% | — | Not comparable |
| MCP AtlasSource | 73.6% | — | Not comparable |
| GDPval-AASource | 1307 | 1158 | DeepSeek V4 Pro (Max) leads |
| ToolathlonSource | 51.8% | 46.3% | DeepSeek V4 Pro (Max) leads |
| AA Agentic IndexSource | 36.4% | 25.6% | DeepSeek V4 Pro (Max) leads |
| APEX-Agents-AASource | 24.3% | 10.6% | DeepSeek V4 Pro (Max) leads |
| τ²-bench resultsSource | 96.2% | 84.8% | DeepSeek V4 Pro (Max) leads |
| GDPval-AASource | 40.4% | 32.9% | DeepSeek V4 Pro (Max) leads |
| AA BriefcaseSource | 932 | — | Not comparable |
| AA EnterpriseOps-GymSource | 40.4% | — | Not comparable |
| AA Harvey LABSource | 84.4% | — | Not comparable |
| AA ITBenchSource | 38.3% | — | Not comparable |
| AA Tau3 BankingSource | 25.8% | — | Not comparable |
| terminalBenchHardSource | 46.2% | — | Not comparable |
| aaTerminalBench21Source | 64% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingDeepSeek V4 Pro (Max) wins14 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| CodeforcesSource | 3206.0 | — | Not comparable |
| SWE-bench VerifiedSource | 80.6% | — | Not comparable |
| SWE-bench ProSource | 55.4% | 56.2% | MiniMax M2.7 leads |
| SWE MultilingualSource | 76.2% | 76.5% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 67.9% | — | Not comparable |
| Vibe Code BenchSource | 49.93% | 27.04% | DeepSeek V4 Pro (Max) leads |
| AA Coding IndexSource | 59.4% | 52.6% | DeepSeek V4 Pro (Max) leads |
| AA-SciCodeSource | 50.0% | 47.0% | DeepSeek V4 Pro (Max) leads |
| 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 |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning4 benchmarks
Knowledge14 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 87.5% | — | Not comparable |
| SimpleQASource | 57.9% | — | Not comparable |
| Chinese-SimpleQASource | 84.4% | — | Not comparable |
| GPQASource | 90.1% | — | Not comparable |
| GPQA-DSource | 90.1% | 87.0% | DeepSeek V4 Pro (Max) leads |
| HLESource | 37.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.3% | 38.1% | DeepSeek V4 Pro (Max) leads |
| AA-GPQA DiamondSource | 88.8% | 87.4% | DeepSeek V4 Pro (Max) leads |
| AA-HLESource | 35.9% | 28.1% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience IndexSource | -10.0% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 43.3% | 26.1% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 34.4% | MiniMax M2.7 leads |
| AA Openness IndexSource | 50.0% | — | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math5 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1264 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.5% | 75.7% | DeepSeek V4 Pro (Max) leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Pro (Max) or MiniMax M2.7?
DeepSeek V4 Pro (Max) and MiniMax M2.7 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 coding, DeepSeek V4 Pro (Max) or MiniMax M2.7?
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 70.9 versus 53.3. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro (Max) or MiniMax M2.7?
DeepSeek V4 Pro (Max) has the edge for agentic tasks in this comparison, averaging 74.5 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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