LFM2.5-24B-A2B vs Qwen3.8 Max Prime
LFM2.5-24B-A2B costs 17.1x less per output token than Qwen3.8 Max Prime ($0.70 vs $12.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.70
- Coding
- No data
- Overall
- No data
- Output $/M
- $12.00
LFM2.5-24B-A2B or Qwen3.8 Max Prime: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick LFM2.5-24B-A2B for output tokens 17.1x cheaper ($0.70 vs $12.00 per million).
No benchmark has tested either model yet, so price and context are the only way to compare them.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Long documents
Listed context window
Qwen3.8 Max Prime
Qwen3.8 Max Prime takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
LFM2.5-24B-A2B
LFM2.5-24B-A2B $0.0004, Qwen3.8 Max Prime $0.010 per turn.
List prices
At a glance
- Coding index
- LFM2.5-24B-A2B not benchmarked, Qwen3.8 Max Prime not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- LFM2.5-24B-A2B $0.70 against Qwen3.8 Max Prime $12.00 per million output tokens, 17.1x apart.
- Context window
- LFM2.5-24B-A2B takes 128K tokens, Qwen3.8 Max Prime 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
LFM2.5-24B-A2B costs less
Repository review
50K input + 3K output tokens
LFM2.5-24B-A2B costs less
Agent loop
200K input + 10K output tokens
LFM2.5-24B-A2B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | LiquidAI LFM2.5-24B-A2B | Qwen3.8 Max Prime |
|---|---|---|
| Maker | Liquid | Qwen |
| Weights | Open weights | No public weights listed |
| Released | October 13, 2026 | September 23, 2026 |
| Context window | 128K | 1M |
| Input $/Mtok | $0.05 | $4.00 |
| Output $/Mtok | $0.70 | $12.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is LFM2.5-24B-A2B better than Qwen3.8 Max Prime for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick LFM2.5-24B-A2B for output tokens 17.1x cheaper ($0.70 vs $12.00 per million).
Which is cheaper, LFM2.5-24B-A2B or Qwen3.8 Max Prime?
LFM2.5-24B-A2B costs $0.05 per million input tokens and $0.70 per million output tokens; Qwen3.8 Max Prime costs $4.00 and $12.00. Cost per solved task is known for neither of them.
Which has the bigger context window, LFM2.5-24B-A2B or Qwen3.8 Max Prime?
Qwen3.8 Max Prime, with 1M tokens against 128K.
Has any benchmark tested both LFM2.5-24B-A2B and Qwen3.8 Max Prime?
No benchmark has tested either model yet, so price and context are the only way to compare them. Data as of October 22, 2026.