Qwen3.5 4B vs Qwen3.5 0.8B vs LFM2 24B A2B
Too close to call on our weighted score (Qwen3.5 4B 57, Qwen3.5 0.8B 57, LFM2 24B A2B 15). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 4B
57/100- ECI—
- Price$0.04 / $0.07
- Context262K
- Alibaba (Qwen)
Qwen3.5 0.8B
57/100- ECI—
- Price—
- Context262K
Liquid AILFM2 24B A2B
15/100- ECI—
- Price$0.03 / $0.12
- Context33K
Too close to call
It is close. Our weighted score puts them within a point (Qwen3.5 4B 57/100, Qwen3.5 0.8B 57/100, LFM2 24B A2B 15/100), so choose by what matters most for your work: Qwen3.5 4B on price. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3.5 4BQwen3.5 4B $0.048 · LFM2 24B A2B $0.052 per 1M tokens (3:1 blend) · Qwen3.5 0.8B unpriced
- Longest contextQwen3.5 4B and Qwen3.5 0.8BQwen3.5 4B 262,144 · Qwen3.5 0.8B 262,144 · LFM2 24B A2B 32,768 tokens
- Widest inputsQwen3.5 4B and Qwen3.5 0.8BQwen3.5 4B: Text, Images, Video · Qwen3.5 0.8B: Text, Images, Video · LFM2 24B A2B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.5 4B | Qwen3.5 0.8B | LFM2 24B A2B |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 70 | 25 |
| Context window | 40% | 37 | 37 | 0 |
| Overall | 100% | 57/100 | 57/100 | 15/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.5 4BAlibaba (Qwen) | Qwen3.5 0.8BAlibaba (Qwen) | LFM2 24B A2BLiquid AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 55.8% | — | — |
| Price per million tokens | |||
| Input | $0.04 | — | $0.03 (best) |
| Output | $0.07 (best) | — | $0.12 |
| Cached input | — | — | — |
| Blended (3:1) | $0.048 (best) | — | $0.052 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 3 providers | — | Median of 1 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 32,768 tokens |
| Max output | 81,920 tokens (best) | 81,920 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | OpenApache-2.0 | OpenLFM Open License v1.0 |
| API model ID | — | — | — |
| API providers | 5 (best) | 1 | 1 |
| Released | Mar 2, 2026 | Mar 2, 2026 | Feb 24, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.5 4B$0.54
- Qwen3.5 0.8B—
- LFM2 24B A2B$0.54
Which should you choose?
Which is better: Qwen3.5 4B, Qwen3.5 0.8B or LFM2 24B A2B?
It is close. Our weighted score puts them within a point (Qwen3.5 4B 57/100, Qwen3.5 0.8B 57/100, LFM2 24B A2B 15/100), so choose by what matters most for your work: Qwen3.5 4B on price. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Qwen3.5 4B, Qwen3.5 0.8B or LFM2 24B A2B?
Qwen3.5 4B is cheaper at $0.04 input / $0.07 output per million tokens (median across 3 API providers). LFM2 24B A2B costs $0.03 input / $0.12 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.048 per million tokens for Qwen3.5 4B versus $0.052 for LFM2 24B A2B (1.1× as much). Qwen3.5 0.8B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.5 4B has not been scored yet, Qwen3.5 0.8B has not been scored yet and LFM2 24B A2B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 4B, Qwen3.5 0.8B and LFM2 24B A2B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.5 4B and Qwen3.5 0.8B have the largest context windows (262,144 and 262,144 tokens), against 32,768 for LFM2 24B A2B. Maximum output per response: Qwen3.5 4B up to 81,920, Qwen3.5 0.8B up to 81,920, LFM2 24B A2B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 4B accepts text, images and video; Qwen3.5 0.8B accepts text, images and video; LFM2 24B A2B accepts text. Qwen3.5 4B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0 and LFM Open License v1.0), so you can self-host them.
Which is newer?
Qwen3.5 4B is the newest, released Mar 2, 2026. Qwen3.5 0.8B came out Mar 2, 2026; LFM2 24B A2B came out Feb 24, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
