Laguna XS 2.1 vs Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B
Gemma 4 12B IT comes out ahead, 75 to 71 and 68 on our weighted score, though Laguna XS 2.1 is 40% cheaper per token.
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
- Our pick
GoogleGemma 4 12B IT
75/100- ECI—
- Price$0.075 / $0.275
- Context262K
NVIDIANemotron 3.5 Lightning 30B A3B
71/100- ECI—
- Price$0.05 / $0.20
- Context262K
Gemma 4 12B IT is our pick
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against Nemotron 3.5 Lightning 30B A3B (71) and Laguna XS 2.1 (68). 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 · Gemma 4 12B IT $0.125 per 1M tokens (3:1 blend)
- Longest contextAbout the sameLaguna XS 2.1 262,144 · Gemma 4 12B IT 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 tokens
- Widest inputsGemma 4 12B ITLaguna XS 2.1: Text · Gemma 4 12B IT: Text, Images · Nemotron 3.5 Lightning 30B A3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Laguna XS 2.1 | Gemma 4 12B IT | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|---|
| Price | 50% | 100 | 93 | 100 |
| Inputs & features | 30% | 35 | 70 | 45 |
| Context window | 20% | 37 | 37 | 37 |
| Overall | 100% | 68/100 | 75/100 | 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 | Gemma 4 12B ITGoogle | 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) | $0.275 | $0.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 (best) | $0.125 | $0.087 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 2 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 | Yes | 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 | Yes | Yes |
| Availability | |||
| Weights | Open | Open | 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 | Jun 9, 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
- Gemma 4 12B IT$1.30
- Nemotron 3.5 Lightning 30B A3B$0.90
Which should you choose?
Which is better: Laguna XS 2.1, Gemma 4 12B IT or Nemotron 3.5 Lightning 30B A3B?
Gemma 4 12B IT is the better all-round choice, scoring 75/100 against Nemotron 3.5 Lightning 30B A3B (71) and Laguna XS 2.1 (68). 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, Gemma 4 12B IT 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); Gemma 4 12B IT costs $0.075 input / $0.275 output per million tokens (median across 2 API providers). 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 $0.125 for Gemma 4 12B IT (1.7× 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, Gemma 4 12B IT 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, Gemma 4 12B IT 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, Gemma 4 12B IT 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, Gemma 4 12B IT 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; Gemma 4 12B IT accepts text and images; Nemotron 3.5 Lightning 30B A3B accepts text. Gemma 4 12B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Nemotron 3.5 Lightning 30B A3B is the newest, released Aug 11, 2026. Laguna XS 2.1 came out Jul 2, 2026; Gemma 4 12B IT came out Jun 9, 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.
