Model comparison
DeepSeek V3 vs MiniMax M2.7
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3 #147 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and MiniMax M2.7 share 16 comparable benchmark results. 1 of 8 categories are comparable. 6 results are unique to DeepSeek V3; 19 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 16
- DeepSeek V3 only
- 6
- MiniMax M2.7 only
- 19
- Comparable categories
- 1 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 1 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 44.97. 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 coding, where it averages 53.3 against 38.9.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.
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 V3 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | DeepSeek V338.9 | Margin→ 14.4 | MiniMax M2.753.3 |
| Agentic | DeepSeek V3Not measured | MarginNo overlap | MiniMax M2.757.0 |
| Knowledge | DeepSeek V372.7 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | DeepSeek V31.7 | MarginNo overlap | MiniMax M2.7Not measured |
| Inst. Following | DeepSeek V386.1 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | MiniMax M2.7$0.3 input / $1.2 output | DeepSeek V3 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | MiniMax M2.7200K | MiniMax M2.7 lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | DeepSeek V3 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.6% | 25.6% | MiniMax M2.7 leads |
| τ²-bench resultsSource | 22.8% | 84.8% | MiniMax M2.7 leads |
| GDPval-AASource | 0.0% | 32.9% | MiniMax M2.7 leads |
| GDPval-AASource | 217 | 1158 | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | — | 57% | Not comparable |
| ToolathlonSource | — | 46.3% | 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 |
CodingMiniMax M2.7 wins13 benchmarks
| Benchmark | DeepSeek V3 | MiniMax M2.7 | Result |
|---|---|---|---|
| LiveCodeBenchSource | 37.6% | — | Not comparable |
| SWE-bench VerifiedSource | 42% | — | Not comparable |
| AA Coding IndexSource | 23.0% | 52.6% | MiniMax M2.7 leads |
| AA-SciCodeSource | 35.4% | 47.0% | MiniMax M2.7 leads |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-bench ProSource | — | 56.2% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| SWE MultilingualSource | — | 76.5% | 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 |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | DeepSeek V3 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 59.1% | — | Not comparable |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 55.7% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 3.6% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -41.3% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 25.4% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 89.4% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math2 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1150 | 1275 | MiniMax M2.7 leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3 or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 44.97.
Which is better for coding, DeepSeek V3 or MiniMax M2.7?
MiniMax M2.7 has the edge for coding in this comparison, averaging 53.3 versus 38.9. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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