Model comparison
MiMo-V2-Omni vs MiniMax M2.7
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiMo-V2-Omni #40 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiMo-V2-Omni and MiniMax M2.7 share 12 comparable benchmark results. 1 of 8 categories are comparable. 2 results are unique to MiMo-V2-Omni; 23 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 12
- MiMo-V2-Omni only
- 2
- MiniMax M2.7 only
- 23
- Comparable categories
- 1 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. MiMo-V2-Omni only becomes the better choice if coding is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 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 has the cleaner BenchAlign overall profile here, landing at 64.11 versus 63.15. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiMo-V2-Omni 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. MiMo-V2-Omni gives you the larger context window at 262K, 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 | MiMo-V2-Omni | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | MiMo-V2-Omni74.8 | Margin← 21.5 | MiniMax M2.753.3 |
| Agentic | MiMo-V2-OmniNot measured | MarginNo overlap | MiniMax M2.757.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiMo-V2-Omni | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiMo-V2-OmniNot available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | MiMo-V2-OmniNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiMo-V2-OmniNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiMo-V2-Omni262K | MiniMax M2.7200K | MiMo-V2-Omni lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | MiMo-V2-Omni | MiniMax M2.7 | Result |
|---|---|---|---|
| Claw-EvalSource | 45.2% | 48.7% | MiniMax M2.7 leads |
| τ²-bench resultsSource | 91.2% | 84.8% | MiMo-V2-Omni 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 |
| 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 |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingMiMo-V2-Omni wins12 benchmarks
| Benchmark | MiMo-V2-Omni | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 36.7% | 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 |
| AA Coding IndexSource | — | 52.6% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | MiMo-V2-Omni | MiniMax M2.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 35.0% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 82.8% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 19.9% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -17.4% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 18.7% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 44.4% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math1 benchmarks
| Benchmark | MiMo-V2-Omni | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | MiMo-V2-Omni | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 53.5% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (2)
Which is better, MiMo-V2-Omni or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 63.15.
Which is better for coding, MiMo-V2-Omni or MiniMax M2.7?
MiMo-V2-Omni has the edge for coding in this comparison, averaging 74.8 versus 53.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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