Ling 3.0 Flash vs Nemotron 3.5 Super VL 120B A12B vs Qwen3.8 Flash
Qwen3.8 Flash comes out ahead, 74 to 71 and 65 on our weighted score, though Ling 3.0 Flash is 2.6× cheaper per token.
inclusionAILing 3.0 Flash
65/100- ECI—
- Price$0.06 / $0.18
- Context262K
NVIDIANemotron 3.5 Super VL 120B A12B
71/100- ECI—
- Price$0.15 / $0.50
- Context1.05M
- Our pick
Alibaba (Qwen)Qwen3.8 Flash
74/100- ECI—
- Price$0.15 / $0.47
- Context1M
Qwen3.8 Flash is our pick
Qwen3.8 Flash is the better all-round choice, scoring 74/100 against Nemotron 3.5 Super VL 120B A12B (71) and Ling 3.0 Flash (65). It leads on inputs & features. Ling 3.0 Flash wins 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 · Qwen3.8 Flash $0.23 · 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 · Qwen3.8 Flash 1,000,000 · Ling 3.0 Flash 262,144 tokens
- Widest inputsNemotron 3.5 Super VL 120B A12B and Qwen3.8 FlashLing 3.0 Flash: Text · Nemotron 3.5 Super VL 120B A12B: Text, Images, Video · Qwen3.8 Flash: Text, Images, Video
- Self-hostingLing 3.0 Flash and Nemotron 3.5 Super VL 120B A12BPublishes downloadable weights (MIT)
| Measure | Weight | Ling 3.0 Flash | Nemotron 3.5 Super VL 120B A12B | Qwen3.8 Flash |
|---|---|---|---|---|
| Price | 50% | 95 | 76 | 77 |
| Inputs & features | 30% | 35 | 70 | 80 |
| Context window | 20% | 37 | 61 | 60 |
| Overall | 100% | 65/100 | 71/100 | 74/100 |
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 Super VL 120B A12BNVIDIA | Qwen3.8 FlashAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 (best) | $0.15 | $0.15 |
| Output | $0.18 (best) | $0.50 | $0.47 |
| Cached input | — | — | $0.016 |
| Blended (3:1) | $0.09 (best) | $0.237 | $0.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 11 providers | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 32,768 tokens | 8,192 tokens | 131,072 tokens (best) |
| 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 | Yeslow · medium · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | Open | Proprietary |
| API model ID | — | nvidia/nemotron-3.5-super-vl-120b-a12b | qwen3.8-flash |
| API providers | 9 | 13 | 28 (best) |
| Released | Jul 23, 2026 | Oct 15, 2026 | Aug 26, 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.
- Ling 3.0 Flash$0.96
- Nemotron 3.5 Super VL 120B A12B$2.50
- Qwen3.8 Flash$2.44
Which should you choose?
Which is better: Ling 3.0 Flash, Nemotron 3.5 Super VL 120B A12B or Qwen3.8 Flash?
Qwen3.8 Flash is the better all-round choice, scoring 74/100 against Nemotron 3.5 Super VL 120B A12B (71) and Ling 3.0 Flash (65). It leads on inputs & features. Ling 3.0 Flash wins 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 Super VL 120B A12B or Qwen3.8 Flash?
Ling 3.0 Flash is cheaper at $0.06 input / $0.18 output per million tokens (median across 7 API providers). Qwen3.8 Flash costs $0.15 input / $0.47 output per million tokens (official Alibaba API price); 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 $0.09 per million tokens for Ling 3.0 Flash versus $0.23 for Qwen3.8 Flash (2.6× as much) and $0.237 for Nemotron 3.5 Super VL 120B A12B (2.6× 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 Super VL 120B A12B has not been scored yet and Qwen3.8 Flash 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 Super VL 120B A12B and Qwen3.8 Flash 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 1,000,000 for Qwen3.8 Flash and 262,144 for Ling 3.0 Flash. Maximum output per response: Ling 3.0 Flash up to 32,768, Nemotron 3.5 Super VL 120B A12B up to 8,192, Qwen3.8 Flash up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.0 Flash accepts text; Nemotron 3.5 Super VL 120B A12B accepts text, images and video; Qwen3.8 Flash accepts text, images and video. Nemotron 3.5 Super VL 120B A12B handles the widest range of inputs.
Are any of these open source?
Ling 3.0 Flash and Nemotron 3.5 Super VL 120B A12B publishes its weights (MIT) and can be self-hosted; Qwen3.8 Flash is proprietary.
Which is newer?
Nemotron 3.5 Super VL 120B A12B is the newest, released Oct 15, 2026. Qwen3.8 Flash came out Aug 26, 2026; Ling 3.0 Flash came out Jul 23, 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.
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