Model comparison
Celeris-1 vs LFM2.5-230M
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. Celeris-1 and LFM2.5-230M share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 5 to LFM2.5-230M.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- LFM2.5-230M only
- 5
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if knowledge is the priority; LFM2.5-230M is the better fit if you want the cheaper token bill or you need the larger 32K context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Celeris-1 and LFM2.5-230M finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Celeris-1 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-230M. That is roughly Infinityx on output cost alone. LFM2.5-230M gives you the larger context window at 32K, compared with 8K for Celeris-1.
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 | Celeris-1 | Δ | LFM2.5-230M |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 54.7 | LFM2.5-230M21.2 |
| Inst. Following | Celeris-1Not measured | MarginNo overlap | LFM2.5-230M50.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
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- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 20.3%Winner: Celeris-1Δ 55.7MMLU-Pro: Celeris-1 scored 75.9%; LFM2.5-230M scored 20.3%. Celeris-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | LFM2.5-230M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | LFM2.5-230M$0 input / $0 output | LFM2.5-230M has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | LFM2.5-230MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | LFM2.5-230MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | LFM2.5-230M32K | LFM2.5-230M lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Celeris-1 | LFM2.5-230M | Result |
|---|---|---|---|
| BFCL v4Source | — | 21.0% | Not comparable |
KnowledgeCeleris-1 wins3 benchmarks
Frequently Asked Questions (2)
Which is better, Celeris-1 or LFM2.5-230M?
Celeris-1 and LFM2.5-230M are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Celeris-1 or LFM2.5-230M?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 21.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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