LiquidAI: LFM2.5-24B-A2B comes out ahead on the data we track: 3 advantages and none for Qwen: Qwen3 235B A22B.
| Spec | LiquidAI: LFM2.5-24B-A2B | Qwen: Qwen3 235B A22B |
|---|---|---|
| Provider | Liquid | Qwen |
| Input price (per 1M tokens) | $0.05 | $0.46 |
| Output price (per 1M tokens) | $0.70 | $1.82 |
| Blended price (3:1 input/output) | $0.21 | $0.80 |
| Cheaper than … of all models we track | 68% | 45% |
| Context window | 128K tokens (~192 pages) | 131K tokens (~197 pages) |
| Larger context than … of all models | 14% | 23% |
| Max output per response | 8K tokens (~6,100 words) | 8K tokens (~6,100 words) |
| Input types | text | text |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-10-13 | 2025-04-28 |
Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
| Workload (per month) | LiquidAI: LFM2.5-24B-A2B | Qwen: Qwen3 235B A22B | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $17.75 | $70.53 | LiquidAI: LFM2.5-24B-A2B saves $52.78 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $13.00 | $72.80 | LiquidAI: LFM2.5-24B-A2B saves $59.80 |
| Coding / tool-using agent 2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens) | $14.40 | $76.44 | LiquidAI: LFM2.5-24B-A2B saves $62.04 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $8.20 | $47.32 | LiquidAI: LFM2.5-24B-A2B saves $39.12 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $3.20 | $24.57 | LiquidAI: LFM2.5-24B-A2B saves $21.37 |
| Capability | LiquidAI: LFM2.5-24B-A2B | Qwen: Qwen3 235B A22B | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | ✓ Yes | return JSON that validates against your schema |
| Reasoning / thinking mode | ✓ Yes | ✓ Yes | spend extra tokens thinking before answering hard problems |
| Image input | — | — | read screenshots, charts and scanned pages |
| Audio input | — | — | take speech or audio directly |
| File / PDF input | — | — | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | — | — | answer from live web results |
| Seed (reproducible sampling) | ✓ Yes | ✓ Yes | repeat a generation for tests and evals |
| Log-probabilities | ✓ Yes | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
LiquidAI: LFM2.5-24B-A2B comes out ahead on the data we track: 3 advantages and none for Qwen: Qwen3 235B A22B.
LiquidAI: LFM2.5-24B-A2B is cheaper: $0.21 vs $0.80 per 1M tokens at a typical 3:1 input/output mix, 73% less. For 50,000 support conversations a month that is $17.75 against $70.53.
LiquidAI: LFM2.5-24B-A2B costs $0.05 per 1M input tokens and $0.70 per 1M output tokens; Qwen: Qwen3 235B A22B costs $0.46 input and $1.82 output. Output tokens are billed separately, so long answers shift the gap.
Qwen: Qwen3 235B A22B: 131K tokens, about 197 pages, against 128K (about 192 pages) for LiquidAI: LFM2.5-24B-A2B.
Tool calling — LiquidAI: LFM2.5-24B-A2B: yes, Qwen: Qwen3 235B A22B: yes. JSON-schema structured output — LiquidAI: LFM2.5-24B-A2B: yes, Qwen: Qwen3 235B A22B: yes.
Neither accepts image input — both are text-in models.
No free variant is listed for either; both are pay-per-token.
LiquidAI: LFM2.5-24B-A2B can write up to 8K tokens (about 6,100 words) per response; Qwen: Qwen3 235B A22B up to 8K (about 6,100 words).
LiquidAI: LFM2.5-24B-A2B was listed on 2026-10-13; Qwen: Qwen3 235B A22B on 2025-04-28.
LFM2.5-24B-A2B is a mixture-of-experts reasoning model from Liquid AI with 24B total and 2.45B active parameters. It always reasons before answering and is built for agents that call tools. Liquid...
Full LiquidAI: LFM2.5-24B-A2B specs →Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...
Full Qwen: Qwen3 235B A22B specs →