Granite 4.2 8B vs Ling 3.0 Flash VL vs Nemotron 3.5 Super VL 120B A12B
Ling 3.0 Flash VL comes out ahead, 76 to 71 and 64 on our weighted score, though Granite 4.2 8B is 8% cheaper per token.
IBMGranite 4.2 8B
64/100- ECI—
- Price$0.06 / $0.15
- Context131K
- Our pick
inclusionAILing 3.0 Flash VL
76/100- ECI—
- Price$0.06 / $0.18
- Context262K
NVIDIANemotron 3.5 Super VL 120B A12B
71/100- ECI—
- Price$0.15 / $0.50
- Context1.05M
Ling 3.0 Flash VL is our pick
Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Nemotron 3.5 Super VL 120B A12B (71) and Granite 4.2 8B (64). Granite 4.2 8B wins on price. Nemotron 3.5 Super VL 120B A12B wins on context window. 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 priceGranite 4.2 8BGranite 4.2 8B $0.083 · Ling 3.0 Flash VL $0.09 · Nemotron 3.5 Super VL 120B A12B $0.237 per 1M tokens (3:1 blend)
- Longest contextNemotron 3.5 Super VL 120B A12BNemotron 3.5 Super VL 120B A12B 1,048,576 · Ling 3.0 Flash VL 262,144 · Granite 4.2 8B 131,072 tokens
- Widest inputsLing 3.0 Flash VL and Nemotron 3.5 Super VL 120B A12BGranite 4.2 8B: Text · Ling 3.0 Flash VL: Text, Images, Video · Nemotron 3.5 Super VL 120B A12B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Granite 4.2 8B | Ling 3.0 Flash VL | Nemotron 3.5 Super VL 120B A12B |
|---|---|---|---|---|
| Price | 50% | 97 | 95 | 76 |
| Inputs & features | 30% | 35 | 70 | 70 |
| Context window | 20% | 25 | 37 | 61 |
| Overall | 100% | 64/100 | 76/100 | 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 | Granite 4.2 8BIBM | Ling 3.0 Flash VLinclusionAI | Nemotron 3.5 Super VL 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 (best) | $0.06 (best) | $0.15 |
| Output | $0.15 (best) | $0.18 | $0.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.083 (best) | $0.09 | $0.237 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 7 providers | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 117,964 tokens (best) | 32,768 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMIT | Open |
| API model ID | — | — | nvidia/nemotron-3.5-super-vl-120b-a12b |
| API providers | 7 | 7 | 13 (best) |
| Released | Aug 25, 2026 | Sep 10, 2026 | Oct 15, 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.
- Granite 4.2 8B$0.90
- Ling 3.0 Flash VL$0.96
- Nemotron 3.5 Super VL 120B A12B$2.50
Which should you choose?
Which is better: Granite 4.2 8B, Ling 3.0 Flash VL or Nemotron 3.5 Super VL 120B A12B?
Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Nemotron 3.5 Super VL 120B A12B (71) and Granite 4.2 8B (64). Granite 4.2 8B wins on price. Nemotron 3.5 Super VL 120B A12B wins on context window. 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 4.2 8B, Ling 3.0 Flash VL or Nemotron 3.5 Super VL 120B A12B?
Granite 4.2 8B is cheaper at $0.06 input / $0.15 output per million tokens (median across 7 API providers). Ling 3.0 Flash VL costs $0.06 input / $0.18 output per million tokens (median across 7 API providers); Nemotron 3.5 Super VL 120B A12B costs $0.15 input / $0.50 output per million tokens (median across 11 API providers; free on Nvidia). At a typical mix of three input tokens to one output token, that is $0.083 per million tokens for Granite 4.2 8B versus $0.09 for Ling 3.0 Flash VL (1.1× as much) and $0.237 for Nemotron 3.5 Super VL 120B A12B (2.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Granite 4.2 8B has not been scored yet, Ling 3.0 Flash VL has not been scored yet and Nemotron 3.5 Super VL 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Granite 4.2 8B, Ling 3.0 Flash VL and Nemotron 3.5 Super VL 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?
Nemotron 3.5 Super VL 120B A12B has the largest context window at 1,048,576 tokens, against 262,144 for Ling 3.0 Flash VL and 131,072 for Granite 4.2 8B. Maximum output per response: Granite 4.2 8B up to 117,964, Ling 3.0 Flash VL up to 32,768, Nemotron 3.5 Super VL 120B A12B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Granite 4.2 8B accepts text; Ling 3.0 Flash VL accepts text, images and video; Nemotron 3.5 Super VL 120B A12B accepts text, images and video. Ling 3.0 Flash VL handles the widest range of inputs.
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 Super VL 120B A12B is the newest, released Oct 15, 2026. Ling 3.0 Flash VL came out Sep 10, 2026; Granite 4.2 8B came out Aug 25, 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 Nov 2, 2026.
