Model comparison
Ling 2.6 Flash vs MiniMax M2.7
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Ling 2.6 Flash #154 (Estimated); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Ling 2.6 Flash and MiniMax M2.7 share 15 comparable benchmark results. 1 of 8 categories are comparable. 3 results are unique to Ling 2.6 Flash; 20 to MiniMax M2.7.
Updated July 21, 2026- Shared results
- 15
- Ling 2.6 Flash only
- 3
- MiniMax M2.7 only
- 20
- Comparable categories
- 1 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 15 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 is clearly ahead on the BenchAlign aggregate, 64.11 to 43.87. 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 27.
Ling 2.6 Flash 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 | Ling 2.6 Flash | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | Ling 2.6 Flash27.0 | Margin→ 26.3 | MiniMax M2.753.3 |
| Agentic | Ling 2.6 FlashNot measured | MarginNo overlap | MiniMax M2.757.0 |
| Knowledge | Ling 2.6 Flash59.0 | MarginNo overlap | MiniMax M2.7Not measured |
| Inst. Following | Ling 2.6 Flash57.0 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Ling 2.6 Flash | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | MiniMax M2.745 tok/s | Ling 2.6 Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | MiniMax M2.72.53 s | Ling 2.6 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | MiniMax M2.7200K | Ling 2.6 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Ling 2.6 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 86% | 84.8% | Ling 2.6 Flash leads |
| GDPval-AASource | 2.2% | 32.9% | MiniMax M2.7 leads |
| GDPval-AASource | 545 | 1158 | MiniMax M2.7 leads |
| AA Agentic IndexSource | 2.3% | 25.6% | 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 wins12 benchmarks
| Benchmark | Ling 2.6 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| SciCodeSource | 27% | — | Not comparable |
| AA Coding IndexSource | 25.3% | 52.6% | MiniMax M2.7 leads |
| AA-SciCodeSource | 27.1% | 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
Knowledge9 benchmarks
| Benchmark | Ling 2.6 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 38.1% | MiniMax M2.7 leads |
| GPQASource | 59% | — | Not comparable |
| AA-GPQA DiamondSource | 59.3% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 6.2% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -65.7% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 15.4% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math1 benchmarks
| Benchmark | Ling 2.6 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Ling 2.6 Flash | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Frequently Asked Questions (2)
Which is better, Ling 2.6 Flash or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 43.87.
Which is better for coding, Ling 2.6 Flash or MiniMax M2.7?
MiniMax M2.7 has the edge for coding in this comparison, averaging 53.3 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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