Model comparison
DeepSeek V4 Flash (Max) vs MiniMax M2.7
Head-to-head evidence from 21 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Flash (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 Flash (Max) and MiniMax M2.7 share 21 comparable benchmark results. 2 of 8 categories are comparable. 24 results are unique to DeepSeek V4 Flash (Max); 14 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 21
- DeepSeek V4 Flash (Max) only
- 24
- MiniMax M2.7 only
- 14
- Comparable categories
- 2 / 8
Treat this as a split decision. DeepSeek V4 Flash (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 21 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 Flash (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 $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). That is roughly 4.3x on output cost alone. DeepSeek V4 Flash (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 Flash (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 Flash (Max) | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V4 Flash (Max)68.8 | Margin← 15.5 | MiniMax M2.753.3 |
| Agentic | DeepSeek V4 Flash (Max)63.8 | Margin← 6.8 | MiniMax M2.757.0 |
| Knowledge | DeepSeek V4 Flash (Max)55.3 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V4 Flash (Max)94.8 | 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 52.6%B 56.2%Winner: MiniMax M2.7Δ 3.6SWE-bench Pro: DeepSeek V4 Flash (Max) scored 52.6%; MiniMax M2.7 scored 56.2%. MiniMax M2.7 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 56.9%B 57%Winner: MiniMax M2.7Δ 0.1Terminal-Bench 2.0: DeepSeek V4 Flash (Max) scored 56.9%; MiniMax M2.7 scored 57%. 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 Flash (Max) | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (Max)$0.14 input / $0.28 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V4 Flash (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (Max)Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (Max)Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (Max)1M | MiniMax M2.7200K | DeepSeek V4 Flash (Max) lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Flash (Max) wins20 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.9% | 57% | MiniMax M2.7 leads |
| BrowseCompSource | 73.2% | — | Not comparable |
| HLE w/ toolsSource | 45.1% | — | Not comparable |
| MCP AtlasSource | 69% | — | Not comparable |
| GDPval-AASource | 1189 | 1158 | DeepSeek V4 Flash (Max) leads |
| ToolathlonSource | 47.8% | 46.3% | DeepSeek V4 Flash (Max) leads |
| AA Agentic IndexSource | 31.1% | 25.6% | DeepSeek V4 Flash (Max) leads |
| τ²-bench resultsSource | 95% | 84.8% | DeepSeek V4 Flash (Max) leads |
| GDPval-AASource | 34.4% | 32.9% | DeepSeek V4 Flash (Max) leads |
| AA BriefcaseSource | 831 | — | Not comparable |
| AA EnterpriseOps-GymSource | 39.6% | — | Not comparable |
| AA Harvey LABSource | 81.3% | — | Not comparable |
| AA ITBenchSource | 31.5% | — | Not comparable |
| AA Tau3 BankingSource | 22.9% | — | Not comparable |
| terminalBenchHardSource | 35.6% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingDeepSeek V4 Flash (Max) wins14 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| CodeforcesSource | 3052.0 | — | Not comparable |
| SWE-bench VerifiedSource | 79% | — | Not comparable |
| SWE-bench ProSource | 52.6% | 56.2% | MiniMax M2.7 leads |
| SWE MultilingualSource | 73.3% | 76.5% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 56.9% | — | Not comparable |
| AA Coding IndexSource | 56.2% | 52.6% | DeepSeek V4 Flash (Max) leads |
| AA-SciCodeSource | 44.9% | 47.0% | MiniMax M2.7 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 |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning4 benchmarks
Knowledge14 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.2% | — | Not comparable |
| SimpleQASource | 34.1% | — | Not comparable |
| Chinese-SimpleQASource | 78.9% | — | Not comparable |
| GPQASource | 88.1% | — | Not comparable |
| GPQA-DSource | 88.1% | 87.0% | DeepSeek V4 Flash (Max) leads |
| HLESource | 34.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.3% | 38.1% | DeepSeek V4 Flash (Max) leads |
| AA-GPQA DiamondSource | 89.4% | 87.4% | DeepSeek V4 Flash (Max) leads |
| AA-HLESource | 32.1% | 28.1% | DeepSeek V4 Flash (Max) leads |
| AA-Omniscience IndexSource | -22.9% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 37.2% | 26.1% | DeepSeek V4 Flash (Max) leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 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 Flash (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 79.2% | 75.7% | DeepSeek V4 Flash (Max) leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Flash (Max) or MiniMax M2.7?
DeepSeek V4 Flash (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 Flash (Max) or MiniMax M2.7?
DeepSeek V4 Flash (Max) has the edge for coding in this comparison, averaging 68.8 versus 53.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Flash (Max) or MiniMax M2.7?
DeepSeek V4 Flash (Max) has the edge for agentic tasks in this comparison, averaging 63.8 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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