Model comparison
DeepSeek V3 vs LFM2.5-8B-A1B
Head-to-head evidence from 13 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
BenchAlign evidence: DeepSeek V3 supported; LFM2.5-8B-A1B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and LFM2.5-8B-A1B share 13 comparable benchmark results. 2 of 8 categories are comparable. 10 results are unique to DeepSeek V3; 5 to LFM2.5-8B-A1B.
Updated July 16, 2026- Shared results
- 13
- DeepSeek V3 only
- 10
- LFM2.5-8B-A1B only
- 5
- Comparable categories
- 2 / 8
Pick DeepSeek V3 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 13 shared benchmark results across 5 evidence categories; 2 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 finishes one point ahead on BenchLM's provisional leaderboard, 38 to 37. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
DeepSeek V3's sharpest advantage is in instruction following, where it averages 86.1 against 68.8. The single biggest benchmark swing on the page is IFEval, 86.1% to 91.8%. LFM2.5-8B-A1B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 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 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 | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | DeepSeek V31.7 | Margin→ 48.3 | LFM2.5-8B-A1B50.0 |
| Inst. Following | DeepSeek V386.1 | Margin← 17.3 | LFM2.5-8B-A1B68.8 |
| Coding | DeepSeek V338.9 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Knowledge | DeepSeek V372.8 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
IFEval
Inst. FollowingA 86.1%B 91.8%Winner: LFM2.5-8B-A1BΔ 5.7IFEval: DeepSeek V3 scored 86.1%; LFM2.5-8B-A1B scored 91.8%. LFM2.5-8B-A1B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | LFM2.5-8B-A1B$0 input / $0 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | LFM2.5-8B-A1B128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding5 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeek V3 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| GPQASource | 59.1% | — | Not comparable |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | 8.3% | DeepSeek V3 leads |
| AA-GPQA DiamondSource | 55.7% | 51.3% | DeepSeek V3 leads |
| AA-HLESource | 3.6% | 6.9% | LFM2.5-8B-A1B leads |
| AA-Omniscience IndexSource | -41.3% | -33.3% | LFM2.5-8B-A1B leads |
| AA-Omniscience AccuracySource | 25.4% | 9.4% | DeepSeek V3 leads |
| AA-Omniscience Hallucination RateSource | 89.4% | 47.0% | LFM2.5-8B-A1B leads |
MathLFM2.5-8B-A1B wins4 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1154 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, DeepSeek V3 or LFM2.5-8B-A1B?
DeepSeek V3 is ahead on BenchLM's provisional leaderboard, 38 to 37. The biggest single separator in this matchup is IFEval, where the scores are 86.1% and 91.8%.
Which is better for math, DeepSeek V3 or LFM2.5-8B-A1B?
LFM2.5-8B-A1B has the edge for math in this comparison, averaging 50 versus 1.7. DeepSeek V3 stays close enough that the answer can still flip depending on your workload.
Which is better for instruction following, DeepSeek V3 or LFM2.5-8B-A1B?
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 68.8. Inside this category, AA-IFBench 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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