Model comparison
DeepSeek V4 Flash 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 Flash #61 (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 and MiniMax M2.7 share 8 comparable benchmark results. 2 of 8 categories are comparable. 14 results are unique to DeepSeek V4 Flash; 27 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 8
- DeepSeek V4 Flash only
- 14
- MiniMax M2.7 only
- 27
- Comparable categories
- 2 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. DeepSeek V4 Flash 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 58.88. 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 49.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 49.1% to 57%. DeepSeek V4 Flash 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. That is roughly 4.3x on output cost alone. DeepSeek V4 Flash 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 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V4 Flash64.2 | Margin← 10.9 | MiniMax M2.753.3 |
| Agentic | DeepSeek V4 Flash49.1 | Margin→ 7.9 | MiniMax M2.757.0 |
| Knowledge | DeepSeek V4 Flash38.8 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V4 Flash40.8 | 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 49.1%B 57%Winner: MiniMax M2.7Δ 7.9Terminal-Bench 2.0: DeepSeek V4 Flash scored 49.1%; MiniMax M2.7 scored 57%. MiniMax M2.7 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 49.1%B 56.2%Winner: MiniMax M2.7Δ 7.1SWE-bench Pro: DeepSeek V4 Flash scored 49.1%; 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 Flash | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash$0.14 input / $0.28 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V4 Flash has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 FlashNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 FlashNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash1M | MiniMax M2.7200K | DeepSeek V4 Flash lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M2.7 wins12 benchmarks
| Benchmark | DeepSeek V4 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 49.1% | 57% | MiniMax M2.7 leads |
| MCP AtlasSource | 64% | — | Not comparable |
| ToolathlonSource | 40.7% | 46.3% | MiniMax M2.7 leads |
| Claw-EvalSource | 57.8% | 48.7% | DeepSeek V4 Flash leads |
| Gert LabsSource | 54.35% | 40.40% | DeepSeek V4 Flash leads |
| 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 Flash wins13 benchmarks
| Benchmark | DeepSeek V4 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.7% | — | Not comparable |
| SWE-bench ProSource | 49.1% | 56.2% | MiniMax M2.7 leads |
| SWE MultilingualSource | 69.7% | 76.5% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 49.1% | — | 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 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| MMLU-ProSource | 83% | — | Not comparable |
| SimpleQASource | 23.1% | — | Not comparable |
| Chinese-SimpleQASource | 71.5% | — | Not comparable |
| GPQASource | 71.2% | — | Not comparable |
| GPQA-DSource | 71.2% | 87.0% | MiniMax M2.7 leads |
| HLESource | 8.1% | — | 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 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.7% | Not comparable |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Flash or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 58.88. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 49.1% and 57%.
Which is better for coding, DeepSeek V4 Flash or MiniMax M2.7?
DeepSeek V4 Flash has the edge for coding in this comparison, averaging 64.2 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 or MiniMax M2.7?
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 49.1. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
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