vakati-tech.ai

// alternatives · October 2026

LFM2.5-24B-A2B alternatives

Cheaper models that come close, budget substitutes, and the cheapest step up — chosen on published LiveBench scores and live list price, never on our opinion. Every model here supports tool calling, as LFM2.5-24B-A2B does.

LiveBench hasn't published a run for LFM2.5-24B-A2B. The strongest benchmarked model at a similar or lower price is Claude Haiku 5.5, scoring 69.8 overall.

LFM2.5-24B-A2B: no published LiveBench run, $0.212 per 1M blended tokens, about $72.00/mo for a RAG assistant.

Benchmarked models at or below its price

LFM2.5-24B-A2B has no published LiveBench run, so these are the highest-scoring benchmarked models priced no more than 25% above it — a known quantity to test against.

ModelOverallvs LFM2.5-24B-A2BBlended / 1MRAG assistantContext
Claude Haiku 5.5anthropic

6% cheaper. Head to head →

69.8—$0.200−6%$74.00/mo1M
MiniMax M3.1 Flashminimax

24% more expensive. Head to head →

66.9—$0.262$102.00/mo1.0M
GPT-6 Lunaopenai

6% cheaper. Head to head →

64.6—$0.200−6%$74.00/mo1.1M
GLM 5.3 Flashz-ai

75% cheaper. Head to head →

62.1—$0.053−75%$34.59/mo1.0M
Nemotron 3.5 Super VLnvidia

12% more expensive. Head to head →

50.2—$0.237$98.00/mo1.0M
Gemma 4 31Bgoogle

18% more expensive. Head to head →

38.7—$0.250$184.00/mo262K

LFM2.5-24B-A2B alternatives FAQ

What is the best alternative to LFM2.5-24B-A2B?

Claude Haiku 5.5. 6% cheaper. Every suggestion supports tool calling, like LFM2.5-24B-A2B.

How alternatives are chosen

  • Scores — LiveBench release 2026-10-25. Only models with a published run are suggested on quality; a missing model means “not evaluated”, not “worse”.
  • Prices — live list prices from OpenRouter, blended 3:1 input:output. Monthly figures price a RAG assistant with the published cached-input rate where there is one.
  • Same job — suggestions must support every capability LFM2.5-24B-A2B has among tool calling and image input. Other listings of the same model (free or batch tiers) are variants, not alternatives, and are left out.

A few points on a public benchmark can hide large differences on your own tasks. Shortlist here, then decide with an eval — how to build one.