Granite-4.0-H-Small vs Ling 3.0 Flash vs Nemotron 3.5 Lightning 30B A3B
Too close to call on our weighted score (Nemotron 3.5 Lightning 30B A3B 68, Ling 3.0 Flash 65, Granite-4.0-H-Small 61). The right pick depends on what you value most.
IBMGranite-4.0-H-Small
61/100- ECI—
- Price$0.064 / $0.265
- Context131K
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
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, Granite-4.0-H-Small 61/100), 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 · Granite-4.0-H-Small $0.114 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 · Granite-4.0-H-Small 131,072 tokens
- Widest inputsSame inputsGranite-4.0-H-Small: Text · Ling 3.0 Flash: Text · Nemotron 3.5 Lightning 30B A3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Granite-4.0-H-Small | Ling 3.0 Flash | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|---|
| Price | 50% | 91 | 95 | 95 |
| Inputs & features | 30% | 35 | 35 | 45 |
| Context window | 20% | 25 | 37 | 37 |
| Overall | 100% | 61/100 | 65/100 | 68/100 |
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 | Granite-4.0-H-SmallIBM | Ling 3.0 FlashinclusionAI | Nemotron 3.5 Lightning 30B A3BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.064 | $0.06 | $0.055 (best) |
| Output | $0.265 | $0.18 (best) | $0.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.114 | $0.09 (best) | $0.091 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official watsonx.ai API | Median of 7 providers | Median of 10 providers |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 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 | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | OpenMIT | Open |
| API model ID | ibm/granite-4-h-small | — | nvidia/nemotron-3.5-lightning-30b-a3b |
| API providers | 1 | 9 | 15 (best) |
| Released | Oct 2, 2025 | Jul 23, 2026 | Aug 11, 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.
- Granite-4.0-H-Small$1.17
- Ling 3.0 Flash$0.96
- Nemotron 3.5 Lightning 30B A3B$0.95
Which should you choose?
Which is better: Granite-4.0-H-Small, Ling 3.0 Flash or Nemotron 3.5 Lightning 30B A3B?
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, Granite-4.0-H-Small 61/100), 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, Granite-4.0-H-Small, Ling 3.0 Flash or Nemotron 3.5 Lightning 30B A3B?
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); Granite-4.0-H-Small costs $0.064 input / $0.265 output per million tokens (official watsonx.ai API price). 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 $0.114 for Granite-4.0-H-Small (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Granite-4.0-H-Small has not been scored yet, Ling 3.0 Flash 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 Granite-4.0-H-Small, Ling 3.0 Flash 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?
Ling 3.0 Flash and Nemotron 3.5 Lightning 30B A3B have the largest context windows (262,144 and 262,144 tokens), against 131,072 for Granite-4.0-H-Small. Maximum output per response: Granite-4.0-H-Small up to 131,072, Ling 3.0 Flash up to 32,768, Nemotron 3.5 Lightning 30B A3B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Granite-4.0-H-Small accepts text; Ling 3.0 Flash accepts text; Nemotron 3.5 Lightning 30B A3B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (MIT), so you can self-host them.
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; Granite-4.0-H-Small came out Oct 2, 2025. 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.
