Model comparison
DeepSeek V3.2 vs LFM2.5-8B-A1B
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: DeepSeek V3.2 supported; LFM2.5-8B-A1B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and LFM2.5-8B-A1B share 12 comparable benchmark results. 1 of 8 categories are comparable. 8 results are unique to DeepSeek V3.2; 6 to LFM2.5-8B-A1B.
Updated July 16, 2026- Shared results
- 12
- DeepSeek V3.2 only
- 8
- LFM2.5-8B-A1B only
- 6
- Comparable categories
- 1 / 8
Pick DeepSeek V3.2 if you want the stronger benchmark profile. LFM2.5-8B-A1B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
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
DeepSeek V3.2 is clearly ahead on the provisional aggregate, 54 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-8B-A1B. That is roughly Infinityx on output cost alone. LFM2.5-8B-A1B is the reasoning model in the pair, while DeepSeek V3.2 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.
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 V3.2 | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin→ 32.9 | LFM2.5-8B-A1B50.0 |
| Coding | DeepSeek V3.260.9 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Inst. Following | DeepSeek V3.2Not measured | MarginNo overlap | LFM2.5-8B-A1B68.8 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | LFM2.5-8B-A1B$0 input / $0 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | LFM2.5-8B-A1B128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 8.3% | DeepSeek V3.2 leads |
| AA-GPQA DiamondSource | 75.1% | 51.3% | DeepSeek V3.2 leads |
| AA-HLESource | 10.5% | 6.9% | DeepSeek V3.2 leads |
| AA-Omniscience IndexSource | -46.7% | -33.3% | LFM2.5-8B-A1B leads |
| AA-Omniscience AccuracySource | 24.2% | 9.4% | DeepSeek V3.2 leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 47.0% | LFM2.5-8B-A1B leads |
MathLFM2.5-8B-A1B wins5 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3.2 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1208 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or LFM2.5-8B-A1B?
DeepSeek V3.2 is ahead on BenchLM's provisional leaderboard, 54 to 37.
Which is better for math, DeepSeek V3.2 or LFM2.5-8B-A1B?
LFM2.5-8B-A1B has the edge for math in this comparison, averaging 50 versus 17.1. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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