Ling 3.0 Flash vs Nemotron 3.5 Lightning 30B A3B vs Schematron V2 Small
Too close to call on our weighted score (Nemotron 3.5 Lightning 30B A3B 68, Ling 3.0 Flash 65, Schematron V2 Small 52). The right pick depends on what you value most.
inclusionAILing 3.0 Flash
65/100- ECI—
- Price$0.06 / $0.18
- Context262K
NVIDIANemotron 3.5 Lightning 30B A3B
68/100- ECI—
- Price$0.055 / $0.20
- Context262K
Inference.net
Schematron V2 Small
52/100- ECI—
- Price$0.05 / $0.23
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Nemotron 3.5 Lightning 30B A3B 68/100, Ling 3.0 Flash 65/100, Schematron V2 Small 52/100), so choose by what matters most for your work: Ling 3.0 Flash 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 priceLing 3.0 FlashLing 3.0 Flash $0.09 · Nemotron 3.5 Lightning 30B A3B $0.091 · Schematron V2 Small $0.095 per 1M tokens (3:1 blend)
- Longest contextLing 3.0 Flash and Nemotron 3.5 Lightning 30B A3BLing 3.0 Flash 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 · Schematron V2 Small 128,000 tokens
- Widest inputsSame inputsLing 3.0 Flash: Text · Nemotron 3.5 Lightning 30B A3B: Text · Schematron V2 Small: Text
- Self-hostingLing 3.0 Flash and Nemotron 3.5 Lightning 30B A3BPublishes downloadable weights (MIT)
| Measure | Weight | Ling 3.0 Flash | Nemotron 3.5 Lightning 30B A3B | Schematron V2 Small |
|---|---|---|---|---|
| Price | 50% | 95 | 95 | 94 |
| Inputs & features | 30% | 35 | 45 | 0 |
| Context window | 20% | 37 | 37 | 24 |
| Overall | 100% | 65/100 | 68/100 | 52/100 |
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 | Ling 3.0 FlashinclusionAI | Nemotron 3.5 Lightning 30B A3BNVIDIA | Schematron V2 SmallInference.net |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 | $0.055 | $0.05 (best) |
| Output | $0.18 (best) | $0.20 | $0.23 |
| Cached input | — | — | — |
| Blended (3:1) | $0.09 (best) | $0.091 | $0.095 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 10 providers | Median of 5 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 128,000 tokens |
| Max output | 32,768 tokens | 262,144 tokens (best) | 4,096 tokens |
| 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 | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenMIT | Open | Proprietary |
| API model ID | — | nvidia/nemotron-3.5-lightning-30b-a3b | — |
| API providers | 9 | 15 (best) | 5 |
| Released | Jul 23, 2026 | Aug 11, 2026 | Apr 16, 2026 |
| Knowledge cutoff | — | Sep 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ling 3.0 Flash$0.96
- Nemotron 3.5 Lightning 30B A3B$0.95
- Schematron V2 Small$0.96
Which should you choose?
Which is better: Ling 3.0 Flash, Nemotron 3.5 Lightning 30B A3B or Schematron V2 Small?
It is close. Our weighted score puts them within 3 points (Nemotron 3.5 Lightning 30B A3B 68/100, Ling 3.0 Flash 65/100, Schematron V2 Small 52/100), so choose by what matters most for your work: Ling 3.0 Flash 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, Ling 3.0 Flash, Nemotron 3.5 Lightning 30B A3B or Schematron V2 Small?
Ling 3.0 Flash is cheaper at $0.06 input / $0.18 output per million tokens (median across 7 API providers). Nemotron 3.5 Lightning 30B A3B costs $0.055 input / $0.20 output per million tokens (median across 10 API providers; free on Nvidia); Schematron V2 Small costs $0.05 input / $0.23 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.09 per million tokens for Ling 3.0 Flash versus $0.091 for Nemotron 3.5 Lightning 30B A3B (1× as much) and $0.095 for Schematron V2 Small (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ling 3.0 Flash has not been scored yet, Nemotron 3.5 Lightning 30B A3B has not been scored yet and Schematron V2 Small has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.0 Flash, Nemotron 3.5 Lightning 30B A3B and Schematron V2 Small yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Schematron V2 Small does not support tool calling, which most coding agents need.
Which has the bigger context window?
Ling 3.0 Flash and Nemotron 3.5 Lightning 30B A3B have the largest context windows (262,144 and 262,144 tokens), against 128,000 for Schematron V2 Small. Maximum output per response: Ling 3.0 Flash up to 32,768, Nemotron 3.5 Lightning 30B A3B up to 262,144, Schematron V2 Small up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Ling 3.0 Flash accepts text; Nemotron 3.5 Lightning 30B A3B accepts text; Schematron V2 Small accepts text. They handle the same number of input types.
Are any of these open source?
Ling 3.0 Flash and Nemotron 3.5 Lightning 30B A3B publishes its weights (MIT) and can be self-hosted; Schematron V2 Small is proprietary.
Which is newer?
Nemotron 3.5 Lightning 30B A3B is the newest, released Aug 11, 2026. Ling 3.0 Flash came out Jul 23, 2026; Schematron V2 Small came out Apr 16, 2026. Knowledge cutoff: Nemotron 3.5 Lightning 30B A3B Sep 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 Nov 2, 2026.
