text model comparison
Beam 1.0 vs Nemotron 3.5 Super VL
Beam 1.0 and Nemotron 3.5 Super VL are both current text models, from Reflection AI and NVIDIA. On Arena Text, Beam 1.0 scores 1436 Elo and Nemotron 3.5 Super VL 1412 Elo (Arena, measured October 28, 2026). Both accept 1,000,000 tokens of context.
- Higher Arena Text
- Beam 1.01436 Elo against 1412 Elo
- Newer release
- Beam 1.0Oct 2026 against Oct 2026
Data verified November 3, 2026 · Dataset 2026-11-03
Side by side
Beam 1.0 and Nemotron 3.5 Super VL, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | Beam 1.0 | Nemotron 3.5 Super VL |
|---|---|---|
| Independent benchmarks | ||
| Arena Text | 1436 Elo (#39 of 51) (better) | 1412 Elo (#45 of 51) |
| Arena WebDev | 1436 Elo (#38 of 45) (better) | 1399 Elo (#41 of 45) |
| Specs | ||
| Provider | Reflection AI | NVIDIA |
| Release date | October 16, 2026 | October 15, 2026 |
| Access | API and open weights | open weights |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | Not reported | Not reported |
| Input | text | text, image, video |
| Output | text | text |
Scores:Arena · 2026-11-03
Charts
Against every current text model
Where Beam 1.0 and Nemotron 3.5 Super VL land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
On Arena Text, Beam 1.0 ranks #39 and Nemotron 3.5 Super VL #45 of 51 current models. Each bar is the share of current models it beats: on score, price and context.
Independent benchmarks
Arena Text
Beam 1.0:#39of 51
1,436 Elo · ahead of 24%
Nemotron 3.5 Super VL:#45of 51
1,412 Elo · ahead of 12%
Arena WebDev
Beam 1.0:#38of 45
1,436 Elo · ahead of 16%
Nemotron 3.5 Super VL:#41of 45
1,399 Elo · ahead of 9%
Price and context
Context window
Beam 1.0:#26of 64
1M tokens · longer than 22%
Nemotron 3.5 Super VL:#26of 64
1M tokens · longer than 22%
Verdict
When to choose each
Beam 1.0
Where it leads
- Higher Arena Text score, 1436 Elo vs 1412 Elo
- Higher Arena WebDev score, 1436 Elo vs 1399 Elo
Best for
- Self-hosted coding agents under a permissive licence
- Fine-tuning or distilling a large open model
Watch out for
- Reflection API is a public beta that is free to start, with no published per-token price
- Text only, with no image, audio or video input
Nemotron 3.5 Super VL
Where it leads
- Accepts image and video input
Best for
- Agents that read screenshots, charts and documents
- Self-hosted multimodal agents on NVIDIA hardware
Watch out for
- NVIDIA calls it 5x faster than the next best open model in its class; that speed claim is not measured independently here
- NVIDIA sells no per-token API for it, so hosted prices come from third parties
From benchmark to production
Building on Beam 1.0 or Nemotron 3.5 Super VL?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
AI Model Cost AuditLaunch price $100, regular price $150What the audit includes
FAQ
Beam 1.0 vs Nemotron 3.5 Super VL FAQ
Which scores higher on independent benchmarks, Beam 1.0 or Nemotron 3.5 Super VL?
Beam 1.0 leads on Arena Text, 1436 Elo to 1412 Elo; Beam 1.0 leads on Arena WebDev, 1436 Elo to 1399 Elo. Scores are independent evaluations, not either vendor's launch figures.
Which has the larger context window, Beam 1.0 or Nemotron 3.5 Super VL?
Both accept 1,000,000 tokens of context.
How do Beam 1.0 and Nemotron 3.5 Super VL differ in features?
Beam 1.0 takes text input; Nemotron 3.5 Super VL takes text, image and video.
Can I run Beam 1.0 or Nemotron 3.5 Super VL myself?
Beam 1.0 is released as open weights you can run yourself; Nemotron 3.5 Super VL is available through its provider's API only.
Further reading
More on Beam 1.0 and Nemotron 3.5 Super VL
Production evaluation
Choosing between Beam 1.0 and Nemotron 3.5 Super VL?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty