LFM2.5-24B-A2B vs Qwen3.6 35B A3B
LFM2.5-24B-A2B costs 1.4x less per output token than Qwen3.6 35B A3B ($0.70 vs $1.00 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
- 45.0
- Output $/M
- $1.00
LFM2.5-24B-A2B or Qwen3.6 35B A3B: 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 1.4x cheaper ($0.70 vs $1.00 per million).
No benchmark has tested both yet. LFM2.5-24B-A2B has none yet; Qwen3.6 35B A3B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead.
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
Intelligence
AA Intelligence Index
No call
Only Qwen3.6 35B A3B has a AA Intelligence Index result, 18.2.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.6 35B A3B has a AA Agentic Index result, 13.1.
Directional only
Long documents
Listed context window
Qwen3.6 35B A3B
Qwen3.6 35B A3B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
LFM2.5-24B-A2B
LFM2.5-24B-A2B $0.0004, Qwen3.6 35B A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- LFM2.5-24B-A2B not benchmarked, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B $1.00 per million output tokens, 1.4x apart.
- Context window
- LFM2.5-24B-A2B takes 128K tokens, Qwen3.6 35B A3B 262K.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 41.9 | Only Qwen3.6 35B A3B measured |
| SciCode | Not measured | 36.6% | Only Qwen3.6 35B A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 35B A3B measured |
| Terminal-Bench | Not measured | 24.6% | Only Qwen3.6 35B A3B measured |
| WeirdML | Not measured | 34.5% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 18.2 | Only Qwen3.6 35B A3B measured |
| Epoch Capabilities Index | Not measured | 143.7 | Only Qwen3.6 35B A3B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 13.1 | Only Qwen3.6 35B A3B measured |
| τ²-Bench Telecom | Not measured | 95.3% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.1% | Only Qwen3.6 35B A3B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 22.2% | Only Qwen3.6 35B A3B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 84.8% | Only Qwen3.6 35B A3B measured |
| Mystery game puzzles | Not measured | 22.0% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.6 35B A3B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 20.4% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | LFM2.5-24B-A2B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| IFBench | Not measured | 64.4% | Only Qwen3.6 35B A3B measured |
| Long context reasoning (AA-LCR) | Not measured | 71.7% | Only Qwen3.6 35B A3B measured |
| DTBench | Not measured | 73.9% | Only Qwen3.6 35B A3B measured |
| LMCA | Not measured | 29.7% | Only Qwen3.6 35B A3B measured |
| Surface Evolver Bench | Not measured | 44.4% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 43 boards here | |||
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 | Qwen3.6 35B A3B |
|---|---|---|
| Maker | Liquid | Qwen |
| Weights | Open weights | Open weights |
| Released | October 13, 2026 | April 27, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.05 | $0.10 |
| Output $/Mtok | $0.70 | $1.00 |
| 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 Qwen3.6 35B A3B 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 1.4x cheaper ($0.70 vs $1.00 per million).
Which is cheaper, LFM2.5-24B-A2B or Qwen3.6 35B A3B?
LFM2.5-24B-A2B costs $0.05 per million input tokens and $0.70 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, LFM2.5-24B-A2B or Qwen3.6 35B A3B?
Qwen3.6 35B A3B, with 262K tokens against 128K.
Has any benchmark tested both LFM2.5-24B-A2B and Qwen3.6 35B A3B?
No benchmark has tested both yet. LFM2.5-24B-A2B has none yet; Qwen3.6 35B A3B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.