Laguna XS 2.1 vs Ling 3.0 Flash Fin vs Nemotron 3.5 Lightning 30B A3B
Nemotron 3.5 Lightning 30B A3B comes out ahead, 71 to 68 and 65 on our weighted score, though Laguna XS 2.1 is 14% cheaper per token.
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
inclusionAILing 3.0 Flash Fin
65/100- ECI—
- Price$0.075 / $0.22
- Context262K
- Our pick
NVIDIANemotron 3.5 Lightning 30B A3B
71/100- ECI—
- Price$0.05 / $0.20
- Context262K
Nemotron 3.5 Lightning 30B A3B is our pick
Nemotron 3.5 Lightning 30B A3B is the better all-round choice, scoring 71/100 against Laguna XS 2.1 (68) and Ling 3.0 Flash Fin (65). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. 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 priceLaguna XS 2.1Laguna XS 2.1 $0.075 · Nemotron 3.5 Lightning 30B A3B $0.087 · Ling 3.0 Flash Fin $0.111 per 1M tokens (3:1 blend)
- Longest contextAbout the sameLaguna XS 2.1 262,144 · Ling 3.0 Flash Fin 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 tokens
- Widest inputsSame inputsLaguna XS 2.1: Text · Ling 3.0 Flash Fin: Text · Nemotron 3.5 Lightning 30B A3B: Text
- Self-hostingLaguna XS 2.1 and Nemotron 3.5 Lightning 30B A3BPublishes downloadable weights
| Measure | Weight | Laguna XS 2.1 | Ling 3.0 Flash Fin | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|---|
| Price | 50% | 100 | 95 | 100 |
| Inputs & features | 30% | 35 | 35 | 45 |
| Context window | 20% | 37 | 37 | 37 |
| Overall | 100% | 68/100 | 65/100 | 71/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Laguna XS 2.1Poolside | Ling 3.0 Flash FininclusionAI | Nemotron 3.5 Lightning 30B A3BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 | $0.075 | $0.05 (best) |
| Output | $0.12 (best) | $0.22 | $0.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 (best) | $0.111 | $0.087 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 1 providers | Median of 9 providers |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 262,144 tokens |
| Max output | 32,768 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | poolside/laguna-xs-2.1 | — | nvidia/nemotron-3.5-lightning-30b-a3b |
| API providers | 4 | 2 | 12 (best) |
| Released | Jul 2, 2026 | Aug 27, 2026 | Aug 11, 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.
- Laguna XS 2.1$0.84
- Ling 3.0 Flash Fin$1.19
- Nemotron 3.5 Lightning 30B A3B$0.90
Which should you choose?
Which is better: Laguna XS 2.1, Ling 3.0 Flash Fin or Nemotron 3.5 Lightning 30B A3B?
Nemotron 3.5 Lightning 30B A3B is the better all-round choice, scoring 71/100 against Laguna XS 2.1 (68) and Ling 3.0 Flash Fin (65). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Laguna XS 2.1, Ling 3.0 Flash Fin or Nemotron 3.5 Lightning 30B A3B?
Laguna XS 2.1 is cheaper at $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside). Nemotron 3.5 Lightning 30B A3B costs $0.05 input / $0.20 output per million tokens (median across 9 API providers; free on Nvidia); Ling 3.0 Flash Fin costs $0.075 input / $0.22 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for Laguna XS 2.1 versus $0.087 for Nemotron 3.5 Lightning 30B A3B (1.2× as much) and $0.111 for Ling 3.0 Flash Fin (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Laguna XS 2.1 has not been scored yet, Ling 3.0 Flash Fin has not been scored yet and Nemotron 3.5 Lightning 30B A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna XS 2.1, Ling 3.0 Flash Fin and Nemotron 3.5 Lightning 30B A3B 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?
Laguna XS 2.1, Ling 3.0 Flash Fin and Nemotron 3.5 Lightning 30B A3B share the same 262,144-token context window. Maximum output per response: Laguna XS 2.1 up to 32,768, Ling 3.0 Flash Fin up to 32,768, Nemotron 3.5 Lightning 30B A3B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Laguna XS 2.1 accepts text; Ling 3.0 Flash Fin accepts text; Nemotron 3.5 Lightning 30B A3B accepts text. They handle the same number of input types.
Are any of these open source?
Laguna XS 2.1 and Nemotron 3.5 Lightning 30B A3B publishes its weights and can be self-hosted; Ling 3.0 Flash Fin is proprietary.
Which is newer?
Ling 3.0 Flash Fin is the newest, released Aug 27, 2026. Nemotron 3.5 Lightning 30B A3B came out Aug 11, 2026; Laguna XS 2.1 came out Jul 2, 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 Oct 4, 2026.
