AI model comparison
Nemotron 3.5 Super VL vs Qwen3.8 27B
Side by side comparison of Nemotron 3.5 Super VL and Qwen3.8 27B on pricing, benchmark scores, context window, and more.
NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total.
- Input / 1M
- $0.15
- Output / 1M
- $0.50
- Context
- 1.05M
Qwen3.8 27B is an open-weight dense vision-language model from Qwen.
- Input / 1M
- $0.04
- Output / 1M
- $1.25
- Context
- 1M
Comparing image generators? Compare image models
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Nemotron 3.5 Super VL
1.3x cheaper for a typical chat
Higher intelligence score
Qwen3.8 27B
20.1 vs 13.6 on Artificial Analysis
Larger context window
Nemotron 3.5 Super VL
1.05M vs 1M tokens
Newer release
Nemotron 3.5 Super VL
Released October 15, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3.8 27B20.1Coding index
Qwen3.8 27B68.1Agentic index
Qwen3.8 27B45.8Pricing
Nemotron 3.5 Super VL vs Qwen3.8 27B API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3.8 27B | |
|---|---|---|
| Input tokensPer 1M tokens | $0.15 | $0.04 |
| Output tokensPer 1M tokens | $0.50 | $1.25 |
| Cached input (read)Per 1M tokens | $0.02 | $0.00 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Qwen3.8 27B$0.70Coding task
30K in, 4K out
Qwen3.8 27B$6.17$5.12 cachedLong document summary
150K in, 2K out
Qwen3.8 27B$8.35$3.09 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3.8 27B | |
|---|---|---|
| Context window | 1.05M tokens | 1M tokens |
| Max output | 236K tokens | 66K tokens |
| Input types | Image, Text, Video | Image, Text, Video |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Extra high, Medium, Low, None |
| Default reasoning effort | Not available | Extra high |
| Release date | October 15, 2026 | August 14, 2026 |
Features
Supported features
API features available for each model.
| Metric | Qwen3.8 27B | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
- Nemotron 3.5 Super VL vs GPT-6 Luna
- Nemotron 3.5 Super VL vs MiniMax M3
- Nemotron 3.5 Super VL vs Inkling Small
- Nemotron 3.5 Super VL vs Inkling
- Nemotron 3.5 Super VL vs Nemotron 3 Ultra
- Nemotron 3.5 Super VL vs Gemini 3.5 Flash Lite
- Nemotron 3.5 Super VL vs Muse Glimmer 30B
- Nemotron 3.5 Super VL vs DeepSeek V4.1 Flash 1017
- Nemotron 3.5 Super VL vs DeepSeek V4.1 Flash
- Nemotron 3.5 Super VL vs GLM 5.3 Flash
- Nemotron 3.5 Super VL vs Hy3
Qwen3.8 27BView full model details- Qwen3.8 27B vs GLM 5.3
- Qwen3.8 27B vs MiMo-V2.6-Pro
- Qwen3.8 27B vs DeepSeek V4.1 Flash 1017
- Qwen3.8 27B vs MiniMax M3.1 Flash
- Qwen3.8 27B vs Step 5 Preview
- Qwen3.8 27B vs GPT-6 Luna
- Qwen3.8 27B vs MiMo-V2.6-Flash
- Qwen3.8 27B vs Mistral Large 4
- Qwen3.8 27B vs DeepSeek V4 Pro 0813
- Qwen3.8 27B vs MiniMax M3
- Qwen3.8 27B vs Inkling Small
- Qwen3.8 27B vs Inkling
- Qwen3.8 27B vs Nemotron 3 Ultra
- Qwen3.8 27B vs Qwen3.8 Flash
- Qwen3.8 27B vs GLM 5.3 FlashX
- Qwen3.8 27B vs Qwen3.6 35B A3B
Our take
Nemotron 3.5 Super VL is 1.3x cheaper for a typical chat. Qwen3.8 27B scores higher on the Artificial Analysis Intelligence Index (20.1 vs 13.6). Nemotron 3.5 Super VL has the larger context window (1.05M vs 1M tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Nemotron 3.5 Super VL or Qwen3.8 27B cheaper?
Nemotron 3.5 Super VL is cheaper for a typical chat (2K input tokens and 500 output tokens). Nemotron 3.5 Super VL costs $0.15 per 1M input tokens and $0.50 per 1M output tokens, while Qwen3.8 27B costs $0.04 per 1M input tokens and $1.25 per 1M output tokens.
Which has a bigger context window, Nemotron 3.5 Super VL or Qwen3.8 27B?
Nemotron 3.5 Super VL supports up to 1.05M tokens of context, compared with 1M for Qwen3.8 27B.
Is Nemotron 3.5 Super VL smarter than Qwen3.8 27B?
On the Artificial Analysis Intelligence Index, Nemotron 3.5 Super VL scores 13.6 and Qwen3.8 27B scores 20.1, putting Qwen3.8 27B ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Nemotron 3.5 Super VL and Qwen3.8 27B at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Nemotron 3.5 Super VL and Qwen3.8 27B answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Nemotron 3.5 Super VL and Qwen3.8 27B at the same time. Compare the answers side by side and keep the best one.