Comparing Nemotron 3.5 Super VL vs Qwen, the honest answer is that Qwen3.8-Flash-Next scores higher on most benchmarks — but Nemotron wins on long context and has a far lower hallucination rate, and it’s free and fast. My video below covers Nemotron. Here’s the side-by-side using Nvidia’s own comparison table, and which model to pick.
Key takeaways
- Qwen3.8-Flash-Next leads on most coding, agent and vision tests.
- Nemotron 3.5 Super VL wins on long context (GraphWalks 89.71) and hallucination rate (30.0).
- Nemotron is roughly level with Qwen3.5-122B on many vision tests.
- Choose by job — and test both.
Nemotron 3.5 Super VL vs Qwen: the scores
| Benchmark | Nemotron 3.5 Super VL | Qwen3.8-Flash-Next | Qwen3.5-122B-A10B |
|---|---|---|---|
| GPQA-Diamond | 89.52 | 92.7 | 86.7 |
| SWE-bench Verified | 71.10 | 78.7 | 71.9 |
| Terminal-Bench 2.1 | 60.67 | 85.80 | 48.70 |
| BrowseComp | 78 | 89.0 | 58.1 |
| CharXiv reasoning | 80.82 | 85.8 | 83.5 |
| OCRBench v2 (English) | 68.12 | 67.9 | 68.3 |
| GraphWalks (long context) | 89.71 | 76.1 | 86.1 |
| AA-Omniscience hallucination rate (lower is better) | 30.0 | 41.3 | 84.5 |
All numbers are from Nvidia’s model card for Nemotron 3.5 Super VL, which includes the Qwen models as comparisons.
Where Qwen wins
Qwen3.8-Flash-Next is ahead on GPQA-Diamond, SWE-bench, Terminal-Bench, BrowseComp and most vision tests, including CharXiv and BabyVision. If you want the highest raw scores from an open model for coding or agent tasks, it’s the stronger pick on paper.
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Where Nemotron 3.5 Super VL wins
- Long context: GraphWalks 89.71 vs 76.1 for Qwen3.8-Flash-Next.
- Fewer hallucinations: a 30.0% hallucination rate on AA-Omniscience, vs 41.3% and 84.5% for the two Qwen models.
- English OCR: level with both Qwen models on OCRBench v2 English.
- Speed: Nvidia says it’s five times faster than the next-best open agentic model in its class.
- Free access: free in OpenCode and via OpenRouter’s free variant.
Which should you use?
| Job | Pick |
|---|---|
| Hardest coding and agent tasks (open models) | Qwen3.8-Flash-Next |
| Long documents and big context | Nemotron 3.5 Super VL |
| Agents acting without supervision | Nemotron 3.5 Super VL (fewer hallucinations) |
| Free, fast visual tasks | Nemotron 3.5 Super VL |
| Running locally on less hardware | A smaller Qwen model |
How I’d use both
In my agent OS I keep several models in separate profiles and route by job. Nemotron 3.5 Super VL handles visual and long-document jobs for free; a top coding model handles hard builds. Switching takes a click, so there’s no reason to pick just one.
Let me be clear: this is not a frontier model. It’s not going to replace Claude Opus 5.5 for your hardest work. What it is: a free, open, fast model that can read screenshots, PDFs and charts and then act on them — which is exactly the job a lot of agent work starts with.
Why hallucination rate matters for agents
When an agent acts on its own — filling in forms, writing reports, updating records — a confident wrong answer is worse than no answer. AA-Omniscience measures how often a model makes things up instead of admitting it doesn’t know. Nemotron 3.5 Super VL’s 30% rate is far lower than the 41.3% and 84.5% for the two Qwen models in Nvidia’s table. For unsupervised agent work, that can matter more than a few extra points on a coding benchmark.
Licences and access
Both families offer open weights, so you can host either yourself. Nemotron 3.5 Super VL uses the OpenMDW-1.1 licence and is free in OpenCode and via OpenRouter’s free variant at the time of writing. Check the licence and current access options for whichever Qwen model you consider, since they vary by model.
A fair test
Run both on the same real tasks from your business: one visual task like reading a report screenshot, one long-document task, and one coding task. Score them on accuracy, speed and how often they needed correcting. That will tell you which belongs in each part of your stack far better than any benchmark table.
Final thoughts
A final thought: the open-model race moves fast, and the leader on any benchmark can change within weeks. Rather than switching your whole stack every time a new model tops a chart, keep a simple routing setup — one profile per model — and update which model handles which job as results change. That way you always get the best tool for each task without rebuilding anything.
For now, Nemotron 3.5 Super VL earns its place for visual, long-document and unsupervised agent work, especially while it’s free.
Test both, keep both, and route by job.
How I’d test it this week
Put both in separate agent profiles and run them on the same week of real work. Track which one you have to correct less often. For agents acting on their own, fewer corrections usually matters more than benchmark points.
Then route jobs by results: whichever model wins each type of task gets it.
Common questions
Can I run both locally? Yes, but both large models need serious hardware; smaller Qwen models are easier to run on consumer machines. Which is better at reading charts? On CharXiv reasoning, Qwen3.8-Flash-Next scores 85.8 to Nemotron’s 80.82. Which is better at reading English text in images? They’re roughly level on OCRBench v2 English. Which should I put in Hermes? Try both in separate profiles; many people keep Nemotron for visual and long-document jobs because it’s free and hallucinates less.
The bottom line on Nemotron 3.5 Super VL vs Qwen
In Nemotron 3.5 Super VL vs Qwen, Qwen3.8-Flash-Next wins most benchmarks, while Nemotron wins on long context, hallucinations, speed and free access. Use both for what they do best.
FAQ: nemotron 3.5 super vl vs qwen
Is Nemotron 3.5 Super VL better than Qwen?
Qwen3.8-Flash-Next scores higher on most benchmarks; Nemotron wins on long context and hallucination rate.
Which hallucinates less?
Nemotron 3.5 Super VL — 30.0% on AA-Omniscience vs 41.3% for Qwen3.8-Flash-Next.
Which is better for coding?
Qwen3.8-Flash-Next on SWE-bench and Terminal-Bench.
Which is better for long documents?
Nemotron 3.5 Super VL, based on GraphWalks.
Where do the numbers come from?
Nvidia’s Nemotron 3.5 Super VL model card.
Which is faster?
Nvidia says Nemotron 3.5 Super VL is five times faster than the next-best open agentic model in its class; speed in practice depends on the host and your hardware.
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About Julian Goldie
I’m Julian Goldie — founder of Goldie Agency, best-selling author, and an AI educator with 428K+ YouTube subscribers. I’ve spent 10+ years in SEO and online business and I test every AI agent on my own work before I recommend it. Join the AI Profit Boardroom for the daily builds, or book a free strategy session to talk through yours.
Related reading
Last updated October 2026. This page is a living guide to nemotron 3.5 super vl vs qwen — the facts here move fast and it is updated as they do.