Model comparison
LFM2.5-8B-A1B 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.
BenchAlign evidence: LFM2.5-8B-A1B estimated; MiniMax M2.7 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-8B-A1B and MiniMax M2.7 share 12 comparable benchmark results. 0 of 8 categories are comparable. 6 results are unique to LFM2.5-8B-A1B; 25 to MiniMax M2.7.
Updated July 16, 2026- Shared results
- 12
- LFM2.5-8B-A1B only
- 6
- MiniMax M2.7 only
- 25
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-8B-A1B and MiniMax M2.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
MiniMax M2.7 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-8B-A1B. MiniMax M2.7 has the larger context window at 200K, compared with 128K for LFM2.5-8B-A1B.
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 | LFM2.5-8B-A1B | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | LFM2.5-8B-A1BNot measured | MarginNo overlap | MiniMax M2.757.0 |
| Coding | LFM2.5-8B-A1BNot measured | MarginNo overlap | MiniMax M2.753.3 |
| Math | LFM2.5-8B-A1B50.0 | MarginNo overlap | MiniMax M2.7Not measured |
| Inst. Following | LFM2.5-8B-A1B68.8 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-8B-A1B | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-8B-A1B$0 input / $0 output | MiniMax M2.7$0.3 input / $1.2 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | LFM2.5-8B-A1BNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-8B-A1BNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-8B-A1B128K | MiniMax M2.7200K | MiniMax M2.7 lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | LFM2.5-8B-A1B | MiniMax M2.7 | Result |
|---|---|---|---|
| BFCL v4Source | 49.7% | — | Not comparable |
| τ²-bench resultsSource | 16.1% | 84.8% | 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 |
| 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 |
Coding12 benchmarks
| Benchmark | LFM2.5-8B-A1B | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench HardSource | 4.5% | 39.4% | MiniMax M2.7 leads |
| AA-SciCodeSource | 7.8% | 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 | LFM2.5-8B-A1B | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-GPQA DiamondSource | 51.3% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 6.9% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -33.3% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 9.4% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 47.0% | 34.4% | MiniMax M2.7 leads |
| Artificial Analysis Intelligence IndexSource | 8.3% | 38.1% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math4 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (3)
Can I compare LFM2.5-8B-A1B and MiniMax M2.7 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for LFM2.5-8B-A1B and MiniMax M2.7 today?
LFM2.5-8B-A1B: $0.00 input / $0.00 output per 1M tokens MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.