LiquidAI
partialShows if the model has enough results for an index.LFM2.5-VL-450M
LFM2.5-VL-450M is a non-reasoning model from LiquidAI. 6 benchmarks count toward its score, in 4 categories.
IndexOverall score out of 100.Unranked
CoverageShare of the index weight with results.50%
SpeedOutput tokens per second.—
Input / 1MUS dollars per 1M input tokens.Free
Output / 1MUS dollars per 1M output tokens.Free
ContextMaximum tokens in one request.128K
EloLMArena rating and rank.N/A
The index is a score out of 100. The ± range shows how much it can change.
CapabilitiesScore per category, out of 100.
Out of 100Results
6 counted| BenchmarkThe test name. | CategoryThe capability that the test measures. | ResultThe score from the publisher. | IndexThis result as a score out of 100. | RunThe settings of the run. | DateDate of the result. | Published byThe source of the result. |
|---|---|---|---|---|---|---|
| CountBench | Multimodal | 73.3% | — | — | — | Qwen |
| Instruction-Following Eval | Instruction | 61.2% | 5.0 | — | — | Jeffrey Zhou et al. |
| RealWorldQA | Multimodal | 58.4% | 5.0 | — | — | Qwen |
| Massive Multi-discipline Multimodal Understanding | Multimodal | 32.7% | 5.0 | — | — | MMMU authors |
| Graduate-Level Google-Proof Q&A | Knowledge | 25.7% | 5.0 | — | — | David Rein et al. |
| Berkeley Function Calling Leaderboard v4 | Agentic | 21.1% | 5.0 | — | — | Arcee AI |
| Massive Multitask Language Understanding Professional | Knowledge | 19.3% | 5.0 | — | — | Yubo Wang et al. |
6 benchmarks count, from 6 of 7 results. A grey row does not count. Too few models took that benchmark.