Model comparison
DeepSeek V4 Flash (High) 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 (High) #92 (Estimated); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (High) and MiniMax M2.7 share 21 comparable benchmark results. 2 of 8 categories are comparable. 17 results are unique to DeepSeek V4 Flash (High); 14 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 21
- DeepSeek V4 Flash (High) only
- 17
- MiniMax M2.7 only
- 14
- Comparable categories
- 2 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. DeepSeek V4 Flash (High) 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 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
MiniMax M2.7 is clearly ahead on the BenchAlign aggregate, 64.11 to 53.95. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7's sharpest advantage is in agentic, where it averages 57 against 55.3. The single biggest benchmark swing on the page is SWE-bench Pro, 52.3% to 56.2%. DeepSeek V4 Flash (High) does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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 (High). That is roughly 4.3x on output cost alone. DeepSeek V4 Flash (High) 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 (High) 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 (High) | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V4 Flash (High)68.5 | Margin← 15.2 | MiniMax M2.753.3 |
| Agentic | DeepSeek V4 Flash (High)55.3 | Margin→ 1.7 | MiniMax M2.757.0 |
| Knowledge | DeepSeek V4 Flash (High)52.1 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V4 Flash (High)91.9 | 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 52.3%B 56.2%Winner: MiniMax M2.7Δ 3.9SWE-bench Pro: DeepSeek V4 Flash (High) scored 52.3%; MiniMax M2.7 scored 56.2%. MiniMax M2.7 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 56.6%B 57%Winner: MiniMax M2.7Δ 0.4Terminal-Bench 2.0: DeepSeek V4 Flash (High) scored 56.6%; 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 (High) | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (High)$0.14 input / $0.28 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V4 Flash (High) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (High)Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (High)Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (High)1M | MiniMax M2.7200K | DeepSeek V4 Flash (High) lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M2.7 wins14 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.6% | 57% | MiniMax M2.7 leads |
| BrowseCompSource | 53.5% | — | Not comparable |
| HLE w/ toolsSource | 40.3% | — | Not comparable |
| MCP AtlasSource | 67.4% | — | Not comparable |
| ToolathlonSource | 43.5% | 46.3% | MiniMax M2.7 leads |
| τ²-bench resultsSource | 95.6% | 84.8% | DeepSeek V4 Flash (High) leads |
| AA Agentic IndexSource | 28.2% | 25.6% | DeepSeek V4 Flash (High) leads |
| GDPval-AASource | 32.4% | 32.9% | MiniMax M2.7 leads |
| GDPval-AASource | 1147 | 1158 | MiniMax M2.7 leads |
| 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 (High) wins14 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | MiniMax M2.7 | Result |
|---|---|---|---|
| CodeforcesSource | 2816.0 | — | Not comparable |
| SWE-bench VerifiedSource | 78.6% | — | Not comparable |
| SWE-bench ProSource | 52.3% | 56.2% | MiniMax M2.7 leads |
| SWE MultilingualSource | 70.2% | 76.5% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| AA-SciCodeSource | 42.0% | 47.0% | MiniMax M2.7 leads |
| AA Coding IndexSource | 52.0% | 52.6% | 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
Knowledge13 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.4% | — | Not comparable |
| SimpleQASource | 28.9% | — | Not comparable |
| Chinese-SimpleQASource | 73.2% | — | Not comparable |
| GPQASource | 87.4% | — | Not comparable |
| GPQA-DSource | 87.4% | 87.0% | DeepSeek V4 Flash (High) leads |
| HLESource | 29.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.5% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 86.7% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 27.8% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -22.3% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 35.5% | 26.1% | DeepSeek V4 Flash (High) leads |
| AA-Omniscience Hallucination RateSource | 89.7% | 34.4% | MiniMax M2.7 leads |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math5 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.5% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Flash (High) or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 53.95. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 52.3% and 56.2%.
Which is better for coding, DeepSeek V4 Flash (High) or MiniMax M2.7?
DeepSeek V4 Flash (High) has the edge for coding in this comparison, averaging 68.5 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 Flash (High) or MiniMax M2.7?
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 55.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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