The Tool Desk
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Short answer: Nemotron 3.5 Super VL is a strong candidate for long-running multimodal agents that must combine OCR, documents, video, reasoning and tool calls, particularly on NVIDIA infrastructure. Its published advantages are a 120-billion-parameter mixture-of-experts design with only 12 billion parameters active per token, a context target of up to 1 million tokens and high throughput in NVIDIA’s own serving tests. Qwen2.5-VL remains the clearest documented open alternative for document extraction, charts, video understanding and visual computer-use tasks. There is no independent, universal head-to-head result proving that either model is best for every agent workload.
What Nemotron 3.5 Super VL is
Nemotron 3.5 Super VL is NVIDIA’s open vision-language model for agents that work across text, images, video, documents and external tools. NVIDIA describes it as a 120B-total/12B-active mixture-of-experts (MoE) hybrid Mamba-Transformer model. “120B total” is the size of the complete parameter pool; “12B active” is the approximate amount used for each token. That distinction matters for serving cost and speed, but it does not make the complete model small enough for ordinary laptop hardware.
The Super model starts from a text-only Nemotron 3 Super base. NVIDIA then combines supervised fine-tuning, reinforcement learning with verifiable rewards (RLVR), specialist-teacher supervision and two phases of multi-teacher on-policy distillation to create the multimodal agent checkpoint. The cookbook identifies C-RADIO v4-H as its vision encoder.
Inputs can include text, still images and video. The model is trained for contexts of up to 1 million tokens, allowing an agent to retain large document sets, long conversations, tool results and visual evidence in one workflow. “Up to” is a training and target-context statement, not a promise that every deployment will sustain the same latency or quality at that length.
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NVIDIA released three relevant checkpoint forms: a base BF16 checkpoint, a post-trained BF16 checkpoint and a post-trained NVFP4 checkpoint. Deployment recipes are provided for vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo, so operators can choose between common open serving stacks and NVIDIA’s optimized inference path.
How it compares with Qwen2.5-VL
Qwen2.5-VL is the most clearly documented open comparator in the available material. Its technical report describes dynamic-resolution image processing, object localization, invoice and form extraction, table and chart interpretation, long-video comprehension with second-level event localization and visual tool use for computer or mobile interaction. It is available in 7B, 32B and 72B sizes.
The 72B Qwen2.5-VL report says its model matches state-of-the-art systems such as GPT-4o and Claude 3.5 Sonnet on the report’s stated tasks. That is a claim about Qwen’s evaluation set, not a controlled comparison with Nemotron 3.5 Super VL. Nemotron’s published numbers likewise come primarily from NVIDIA tests and should not be read as a universal ranking.
| Attribute | Nemotron 3.5 Super VL | Qwen2.5-VL |
|---|---|---|
| Model family | Hybrid Mamba-Transformer MoE vision-language model | Vision-language family with 7B, 32B and 72B variants |
| Parameter information | 120B total; approximately 12B active per token | 7B, 32B and 72B model sizes reported |
| Modalities | Text, images, video, document understanding and visual reasoning | Text, images, video, documents, charts, diagrams and visual computer/mobile interaction |
| Context target | Up to 1M tokens during training and intended long-context use | Long-video and long-context capabilities are described, but the supplied report does not establish a directly comparable context limit |
| Agent emphasis | Coding, web research, document intelligence, cybersecurity and video understanding | Visual tool use, extraction, localization, chart/diagram analysis and video event localization |
| Serving information | Recipes for vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo; BF16 and NVFP4 post-trained checkpoints | Variant-specific deployment requirements; the supplied material does not establish an equivalent NVIDIA throughput comparison |
The practical choice is therefore workload-dependent. Nemotron offers an integrated long-context agent design and documented NVIDIA serving efficiency. Qwen offers smaller sizes and a detailed record of visual extraction and grounding tasks, which can make experimentation easier when a 7B or 32B model is sufficient.
Which model is better for AI agents?
There is no responsible one-word winner. An agent combines perception, planning, tool selection, execution and recovery. A model can excel at reading a table yet fail to recover from a malformed API response, or score well on coding while missing a small visual detail. Compare the models against the complete workflow you intend to run.
Choose Nemotron when the workflow is long and multimodal
- The agent must carry large collections of pages, screenshots, video segments and tool outputs through many turns.
- You want one checkpoint designed for text, image, video and document reasoning instead of a separate vision model and planner.
- Your serving fleet uses NVIDIA GPUs and can exploit the published TensorRT-LLM, Dynamo or other NVIDIA-optimized paths.
- You need a model aimed at coding, web research, cybersecurity or other tool-rich, long-horizon jobs.
Choose Qwen2.5-VL when visual extraction and grounding dominate
- The central task is invoice, form, table, chart or diagram extraction and you can validate against Qwen’s documented capabilities.
- You need a smaller 7B or 32B starting point before committing to a larger model.
- The agent must localize objects precisely or identify events at particular seconds in a long video.
- You require visual interaction with a computer or mobile interface and want a report that explicitly covers that use case.
Do not decide from parameter count alone
Nemotron’s 12B active-parameter figure describes per-token computation, not the memory needed to hold the complete 120B expert pool. Conversely, Qwen’s 72B label does not by itself predict extraction accuracy, tool-call reliability or latency. Measure the model with your images, documents, context lengths, concurrency and tool harness.
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What the published performance numbers actually show
NVIDIA reports 51.78% on SWE-bench Pro and 71.9% on Terminal-Bench 2.1. The Terminal-Bench figure includes parallel agent execution and self-verification, so it is a system result rather than a pure single-pass language-model score. NVIDIA also reports 4,856 output tokens per second per GPU on a single GB200 in an evaluated 20-turn agentic workload.
Under NVIDIA’s stated interactive serving conditions, Nemotron is reported at 2.34 times the throughput of Qwen3.5 122B-A10B and 1.90 times that of Qwen3.8 Flash-Next. NVIDIA’s technical blog additionally reports more than 600 output tokens per second on an Artificial Analysis agentic endpoint and notes that 12B parameters activate per token. These figures are vendor or setup-specific; they are not guarantees for another GPU, quantization, context length or concurrency level.
| Published result | What it measures | Qualification |
|---|---|---|
| 51.78% SWE-bench Pro | Software-engineering task performance | NVIDIA report, 2026; harness and system configuration matter |
| 71.9% Terminal-Bench 2.1 | Terminal-agent task completion | NVIDIA report, 2026; includes parallel execution and self-verification |
| 4,856 output tokens/s/GPU | Generation speed in a 20-turn agentic workload | Single GB200; NVIDIA evaluation conditions |
| 2.34× Qwen3.5 122B-A10B | Relative throughput | NVIDIA interactive-serving comparison; not a hardware-independent ratio |
| 1.90× Qwen3.8 Flash-Next | Relative throughput | NVIDIA interactive-serving comparison; workload and serving stack are material |
| >600 output tokens/s | Agentic endpoint speed | NVIDIA technical-blog report; endpoint and conditions are specific |
| 63.44% vs. 56.33% | Hermes Bench: Nemotron 3.5 Super VL versus Nemotron 3 Ultra | NVIDIA technical blog, 2026 |
| 87.05% vs. 84.3% | LangChain evaluation: Nemotron 3.5 Super VL versus Nemotron 3 Ultra | NVIDIA technical blog, 2026 |
Use these numbers to form hypotheses, not purchasing promises. A fair reproduction records the GPU model, number of GPUs, BF16 or NVFP4 precision, batch size, concurrency, prompt and output lengths, image and video preprocessing, context length, tool latency, cache reuse and whether the score comes from a vendor-run or independently reproduced harness.
Where Nemotron’s visual abilities matter
OCR-heavy pages and scanned documents
NVIDIA specifically targets optical character recognition, tables, charts and structured information in visual documents. That is useful when a page contains text that is positioned, colored or formatted in ways that plain OCR loses. An agent can retain the page image, extracted text and subsequent tool results in the same long context, then cite or transform the information in a later step.
For production document work, test skewed scans, stamps, handwriting, multi-column layouts, footnotes, tiny type and pages with both images and tables. A model that reads a clean benchmark PDF may still misassociate a row label with the wrong value. Keep a deterministic validation step for totals, dates, identifiers and required fields.
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Chart understanding requires more than recognizing text. The model must connect legends, axes, units, color and geometry before answering a question. Test bar, line, stacked, logarithmic and dual-axis charts separately. Ask for the extracted series and the reasoning behind a conclusion, then compare it with a machine-readable source when one exists.
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Video and temporal evidence
Nemotron accepts video and is positioned for video understanding. Qwen2.5-VL’s report explicitly describes second-level event localization in long videos. For either model, measure timestamp accuracy, missed short events, repeated scenes and performance when the relevant evidence is separated by long intervals. Video tokenization, frame sampling and context limits can dominate cost even when the language model itself is fast.
Agent behavior: the tests that matter beyond vision
A useful agent benchmark should score the whole loop, not only the final answer. Give both models identical tools, permissions, prompts, context windows and retry limits.
- Tool-call validity: Count malformed JSON, wrong function names, missing required fields and invented arguments.
- Grounding: Check whether each argument is supported by the supplied page, image, video frame or tool result.
- Planning: Measure whether the model chooses a sensible sequence instead of repeatedly calling the same tool.
- Error recovery: Return realistic failures such as timeouts, permission errors and empty search results, then record whether the agent changes strategy.
- Long-horizon retention: Put a critical constraint early in a long document and test whether it is still respected after many tool calls.
- Verification: Require the agent to check totals, file contents or test results before claiming success.
- Cost and latency: Record time to first token, end-to-end completion time, generated tokens, image/video preprocessing and GPU utilization.
NVIDIA reports higher scores for Nemotron 3.5 Super VL than Nemotron 3 Ultra on Hermes Bench (63.44% versus 56.33%) and LangChain evaluations (87.05% versus 84.3%). Those comparisons show an improvement within NVIDIA’s model line; they do not establish a Qwen head-to-head result.
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Can you run Nemotron 3.5 Super VL locally?
You can deploy the released checkpoints on infrastructure you control, but “locally” should not be confused with “on a typical personal computer.” The complete 120B MoE still has to be stored, even though only about 12B parameters are active for each token.
Rough weight-memory arithmetic
At two bytes per parameter, 120B parameters require roughly 240 GB just for BF16 weights before runtime buffers, the vision encoder, the key-value cache and framework overhead. A four-bit representation has a rough 60 GB weight-only floor, but NVFP4 packing, scales, activations, cache size and implementation overhead add memory. These are arithmetic estimates, not NVIDIA’s stated minimum hardware requirements. Long contexts and multiple concurrent requests can require substantially more memory.
| Checkpoint or factor | What it implies |
|---|---|
| 120B total parameters | Memory planning must account for the full expert pool, not only the 12B active subset |
| 12B active parameters per token | Per-token compute can be lower than a dense 120B model, especially with optimized MoE serving |
| BF16 | Highest listed precision; roughly 240 GB of weight storage before overhead by simple two-byte arithmetic |
| NVFP4 | Lower-precision post-trained checkpoint intended to reduce weight memory and improve throughput; actual footprint depends on the serving implementation |
| 1M-token target context | Potentially very large key-value cache and lower throughput at long inputs; measure rather than assume |
Deployment checklist
- Select the checkpoint: Use base BF16 for a foundation model, post-trained BF16 for the released agent behavior, or post-trained NVFP4 when your hardware and software support that format.
- Choose a serving stack: NVIDIA provides recipes for vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo. Verify the exact model revision, multimodal support and MoE/NVFP4 compatibility before scheduling a production rollout.
- Plan GPU memory: Reserve space for all weights, the C-RADIO v4-H vision encoder, runtime buffers, conversation cache and simultaneous requests. Do not size a cluster from active parameters alone.
- Define the context budget: Set separate limits for text, images, video frames and tool output. A nominal 1M-token capability does not mean every request should use 1M tokens.
- Instrument the service: Log time to first token, output rate, queue time, GPU memory, cache hit rate, image/video preprocessing time and tool-call latency.
- Run acceptance tests: Use representative PDFs, screenshots, videos and failed-tool scenarios, then compare accuracy and end-to-end cost with your current model.
Hardware availability is a practical constraint. NVIDIA’s published speed figures use a GB200 and an NVIDIA serving environment; the same ratio should not be expected on non-NVIDIA hardware or an unoptimized runtime.
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What the customer case studies do—and do not—prove
NVIDIA reports that Scale AI post-trained Nemotron for vendor-credit processing and measured 40 times lower inference cost and five times lower latency for that workload. Ramp reported expectations of up to 50% higher throughput and 10 times lower latency for receipt-to-JSON processing. Both are customer-specific case studies. They indicate that targeted post-training and an appropriate deployment can materially change economics, but they are not general guarantees for every document or receipt pipeline.
If your use case resembles one of these workflows, reproduce the measurement with your own page mix, validation rules, concurrency and infrastructure. Report whether “cost” includes image processing, retries, storage, orchestration and human review.
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Build a representative test set
Include clean and degraded scans, multilingual pages if relevant, dense tables, charts with legends, screenshots of software interfaces, short and long videos, contradictory documents and tool failures. Label the fields, locations, timestamps and acceptable answer variations that matter to your business.
Run matched experiments
Keep prompts, tools, permissions, temperature, maximum output, context policy and retry logic constant. Run enough repetitions to expose intermittent tool errors. Test short prompts and long histories separately; a model can look fast in a short chat and slow when the cache is full.
Score business outcomes
Combine exact extraction accuracy, spatial or timestamp accuracy, tool success rate, recovery rate, human-correction time, latency and fully loaded cost. For coding agents, record tests passed and repository changes accepted rather than relying on generated-token speed.
Choose the smallest model that meets the target
Qwen2.5-VL’s 7B and 32B variants may be more practical for a narrowly scoped visual extractor. Nemotron’s large MoE may justify its infrastructure when one agent must reason across modalities and many steps. A smaller model with deterministic validation can be preferable to a larger model whose extra capability is not used.
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Common pitfalls and fixes
Confusing active parameters with memory requirements
Symptom: The service cannot load despite a 12B active-parameter estimate. Fix: budget for the complete 120B expert pool, precision overhead, vision components and cache; then verify the chosen runtime’s sharding and quantization support.
Treating vendor throughput as a universal speed claim
Symptom: Production throughput is far below 4,856 tokens per second per GPU. Fix: reproduce the same GPU class, precision, context, concurrency, output length and serving stack before attributing the difference to the model.
Ignoring visual preprocessing
Symptom: Generation is fast but end-to-end document latency is poor. Fix: measure decoding, image resizing, frame sampling, OCR-related work and tool calls as separate stages.
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Symptom: The agent misses an instruction buried in a very large history. Fix: summarize stale tool output, preserve authoritative fields in a compact state object and test retrieval at the context lengths you will actually operate.
Assuming extraction equals verification
Symptom: The model produces plausible but incorrect totals or identifiers. Fix: validate arithmetic and required fields against source data, and route low-confidence or conflicting pages to review.
Decision summary
| Your priority | Starting choice | Reason |
|---|---|---|
| Long multimodal agent sessions on NVIDIA infrastructure | Nemotron 3.5 Super VL | 1M-token target, integrated text/image/video design, 12B active MoE path and NVIDIA serving recipes |
| OCR, invoices, forms, tables and charts with a smaller entry point | Qwen2.5-VL 7B or 32B | Document-focused capabilities and smaller published variants |
| Large visual agent with object grounding and long-video event timestamps | Benchmark both; begin with Qwen2.5-VL if those report-described features are central | Qwen’s technical report explicitly covers these tasks, while Nemotron’s results need workload-specific validation |
| Maximum throughput on a GB200-backed service | Nemotron 3.5 Super VL | NVIDIA reports 4,856 output tokens/s/GPU in a defined 20-turn workload |
| Non-NVIDIA or mixed hardware | Run a matched pilot before choosing | The documented speed advantages are tied to NVIDIA hardware and serving conditions |
Nemotron 3.5 Super VL is best viewed as a high-capacity, agent-oriented multimodal platform rather than a universally superior VLM. Its combination of long-context training, visual inputs, MoE efficiency and NVIDIA deployment support is compelling for complex workflows. Qwen2.5-VL is a credible open alternative when visual extraction, localization, video events or a smaller model size are more important. The defensible choice comes from a matched evaluation of your documents, videos, tools, context lengths and hardware—not from a single benchmark percentage or parameter label.
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