Laguna XS 2.1 vs Mercury 2.5 vs Nemotron 3 Super 120B A12B
Too close to call on our weighted score (Mercury 2.5 71, Laguna XS 2.1 68, Nemotron 3 Super 120B A12B 54). The right pick depends on what you value most.
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
InceptionMercury 2.5
71/100- ECI—
- Price$0.04 / $0.15
- Context260K
NVIDIANemotron 3 Super 120B A12B
54/100- ECI—
- Price$0.20 / $0.80
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (Mercury 2.5 71/100, Laguna XS 2.1 68/100, Nemotron 3 Super 120B A12B 54/100), so choose by what matters most for your work: Mercury 2.5 on price. 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 priceMercury 2.5Mercury 2.5 $0.068 · Laguna XS 2.1 $0.075 · Nemotron 3 Super 120B A12B $0.35 per 1M tokens (3:1 blend)
- Longest contextAbout the sameLaguna XS 2.1 262,144 · Nemotron 3 Super 120B A12B 262,144 · Mercury 2.5 260,000 tokens
- Widest inputsSame inputsLaguna XS 2.1: Text · Mercury 2.5: Text · Nemotron 3 Super 120B A12B: Text
- Self-hostingLaguna XS 2.1 and Nemotron 3 Super 120B A12BPublishes downloadable weights
| Measure | Weight | Laguna XS 2.1 | Mercury 2.5 | Nemotron 3 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 72 |
| Inputs & features | 30% | 35 | 45 | 35 |
| Context window | 20% | 37 | 36 | 37 |
| Overall | 100% | 68/100 | 71/100 | 54/100 |
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 | Mercury 2.5Inception | Nemotron 3 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 | $0.04 (best) | $0.20 |
| Output | $0.12 (best) | $0.15 | $0.80 |
| Cached input | — | $0.004 | — |
| Blended (3:1) | $0.075 | $0.068 (best) | $0.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Inception API | Official Nvidia API |
| Limits | |||
| Context window | 262,144 tokens (best) | 260,000 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 65,536 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 | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | poolside/laguna-xs-2.1 | mercury-2.5 | nvidia/nemotron-3-super-120b-a12b |
| API providers | 4 | 1 | 19 (best) |
| Released | Jul 2, 2026 | Sep 8, 2026 | Mar 11, 2026 |
| Knowledge cutoff | — | Nov 1, 2025 | — |
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
- Mercury 2.5$0.70
- Nemotron 3 Super 120B A12B$3.60
Which should you choose?
Which is better: Laguna XS 2.1, Mercury 2.5 or Nemotron 3 Super 120B A12B?
It is close. Our weighted score puts them within 3 points (Mercury 2.5 71/100, Laguna XS 2.1 68/100, Nemotron 3 Super 120B A12B 54/100), so choose by what matters most for your work: Mercury 2.5 on price. 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, Mercury 2.5 or Nemotron 3 Super 120B A12B?
Mercury 2.5 is cheaper at $0.04 input / $0.15 output per million tokens (official Inception API price). Laguna XS 2.1 costs $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside); Nemotron 3 Super 120B A12B costs $0.20 input / $0.80 output per million tokens (official Nvidia API price). At a typical mix of three input tokens to one output token, that is $0.068 per million tokens for Mercury 2.5 versus $0.075 for Laguna XS 2.1 (1.1× as much) and $0.35 for Nemotron 3 Super 120B A12B (5.2× 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, Mercury 2.5 has not been scored yet and Nemotron 3 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna XS 2.1, Mercury 2.5 and Nemotron 3 Super 120B A12B 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?
Their context windows are effectively the same size: Laguna XS 2.1 262,144, Mercury 2.5 260,000 and Nemotron 3 Super 120B A12B 262,144 tokens. Maximum output per response: Laguna XS 2.1 up to 32,768, Mercury 2.5 up to 65,536, Nemotron 3 Super 120B A12B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Laguna XS 2.1 accepts text; Mercury 2.5 accepts text; Nemotron 3 Super 120B A12B accepts text. They handle the same number of input types.
Are any of these open source?
Laguna XS 2.1 and Nemotron 3 Super 120B A12B publishes its weights and can be self-hosted; Mercury 2.5 is proprietary.
Which is newer?
Mercury 2.5 is the newest, released Sep 8, 2026. Laguna XS 2.1 came out Jul 2, 2026; Nemotron 3 Super 120B A12B came out Mar 11, 2026. Knowledge cutoff: Mercury 2.5 Nov 1, 2025.
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.
