Nemotron 3.5 Super VL vs Claude Opus 5.5: 26 vs 58 Index

Nemotron 3.5 Super VL vs Claude Opus 5.5: $0.25 vs $5.98 per Index Task

Nemotron 3.5 Super VL is the faster and far cheaper option: $0.25 vs $5.98 per Artificial Analysis Intelligence Index task, and 196 vs 97 output tokens per second. Claude Opus 5.5 is much more capable, scoring 58 vs 26 on the same index. Which one you should pick depends on whether you need frontier-level general reasoning or high-volume multimodal agent work that you can self-host. NVIDIA released Nemotron 3.5 Super VL on October 15, 2026, as an open-weight 120B-parameter mixture-of-experts model with 12B active parameters. Opus 5.5 is Anthropic's hosted flagship and currently sits at the top of the Artificial Analysis chart.

Key Takeaways

• Intelligence gap: Claude Opus 5.5 (max with fallback) scores 58 on the Artificial Analysis Intelligence Index. Nemotron 3.5 Super VL scores 26, which ranks #1 of 67 models in its own class.
• Cost gap: On Artificial Analysis, one Intelligence Index task costs $0.25 on Nemotron 3.5 Super VL and $5.98 on Opus 5.5, about 24x more.
• Speed gap: Nemotron 3.5 Super VL outputs 196 tokens/s against 97 tokens/s for Opus 5.5, according to the same leaderboard.
• Openness: Nemotron 3.5 Super VL ships open weights (BF16 and NVFP4) under the OpenMDW-1.1 license. Opus 5.5 is available only through hosted platforms.
• The "beats Claude" headline needs a caveat. VentureBeat reports that a post-trained Super VL beat Claude Opus 5 on specialized task benchmarks. That was Opus 5, not Opus 5.5, and the result came after customization.
• Head-to-head data is thin. The only shared ground truth is the Artificial Analysis index, and every other comparison cell below is marked accordingly.

Nemotron 3.5 Super VL vs Claude Opus 5.5 at a Glance

Dimension	Nemotron 3.5 Super VL	Claude Opus 5.5
AA Intelligence Index	26 (official AA listing)	58, max with fallback (official AA listing)
Output speed (AA)	196 tokens/s	97 tokens/s
Cost per AA Index task	$0.25	$5.98
API price per 1M tokens	$0.20 in / $0.575 out (AA median provider)	$8 in / $40 out for fast mode (news roundup; standard tier not confirmed)
Context window	1M tokens	Not yet confirmed
Weights	Open, OpenMDW-1.1	Closed, hosted only
Inputs	Text, images, documents, GUIs, video	Not yet confirmed
Architecture	120B MoE, 12B active, hybrid Mamba-Transformer	Not disclosed

The Artificial Analysis model page is the only source that measures both models on one methodology. Every other Opus 5.5 cell is either from a news roundup or not yet confirmed.

Benchmarks: Where the 32-Point Gap Comes From

On the Artificial Analysis Intelligence Index v4.3.2, Opus 5.5 leads Nemotron 3.5 Super VL by 32 points, 58 to 26. The index combines 10 evaluations, including Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDPval-AA and AA-Omniscience. Nemotron 3.5 Super VL sits behind every frontier model on the chart, including Claude Fable 5.1 (53), GPT-6 Astra (53) and DeepSeek V4.1 Flash (39).

The 32-point gap is the single most important number in this comparison: Opus 5.5 is a frontier generalist, and Nemotron 3.5 Super VL is a class-leading efficient specialist.

Nemotron's own numbers are respectable for a model with 12B active parameters. Artificial Analysis component scores include 30.7% on Humanity's Last Exam, 47.8% on SciCode and 69.0% on long-context reasoning (LCR). NVIDIA reports a 51.78% pass rate on SWE-bench Pro, below Qwen3.8 Flash-Next at 60.37%. The Opus 5.5 SWE-bench Pro score does not appear in these posts. Component benchmarks for Opus 5.5 are not yet confirmed in the sources reviewed.

Early third-party results are mixed:

• KiloBench: an early build scored 53%, the same as GLM 5.2, per Kilo.

Source: @kilocode

• LangChain Deep Agents suite: an 87% average pass rate, according to LangChain's launch-day post.
• Plotly data analysis test: 30 of 43 questions correct with a 12-second median per question. The model missed all 3 data-quality questions and all 5 hidden-pattern questions, according to community reporting.

The post-training caveat

The VentureBeat headline, "Nemotron 3.5 Super VL with post-training beats Claude Opus 5, GPT-6.1 Sol" <!-- rel:dofollow class:authoritative -->, covers five narrow evaluations: document intelligence, security attack resilience, financial receipt extraction, scientific experimentation and medical insurance claims. Those wins required customer-specific fine-tuning, and the comparison point was Opus 5. A separate partner case study from Trajectory Labs claimed parity with Claude Opus 4.7 on Harvey's Legal Agent Bench at $1.34 per million tokens vs $15. Neither result is evidence about Opus 5.5 out of the box.

Speed and Efficiency

Nemotron 3.5 Super VL is built for throughput. Artificial Analysis lists it at 196 tokens/s, the #5 speed of 27 models in its class. Opus 5.5 runs at 97 tokens/s on the same chart. Community posts cite Artificial Analysis measuring about 630 tokens/s with 100K-token inputs. NVIDIA says the model reaches "over 600 TPS" on a Baseten endpoint.

The efficiency stack has four named components:

• Multi-Token Prediction (MTP): generates several candidate tokens per forward pass.
• NVFP4 quantization: 4-bit weights and activations on Blackwell GPUs. NVFP4 KV cache quantization shrinks the cache's memory footprint.
• LiLiCorr speculative decoding: up to 2.3x faster generation on DGX Spark, according to launch coverage.
• Context caching: reuses attention caches and Mamba states across repeated agent calls.

Source: @ItsmeAjayKV

Anthropic's answer on speed is Opus 5.5 fast mode. A news roundup reports it is now generally available on the Claude Platform with no waitlist, available on Microsoft Foundry, and coming to Google Cloud. It runs up to 2.5x faster than standard Opus 5.5. No independent tokens-per-second figure for fast mode has been published, so a fast-mode vs Nemotron speed comparison is still unverified.

Verbosity is one point against Nemotron. It produced 170M output tokens while running the Intelligence Index, against a median of 100M for comparable models. That eats into its price advantage on long tasks.

Pricing and Total Cost

At $0.25 per Intelligence Index task against $5.98, Nemotron 3.5 Super VL completes roughly 24 benchmark tasks for the price of one Opus 5.5 task.

Nemotron's listed API price is $0.20 per 1M input tokens and $0.575 per 1M output tokens, with a 73% cache discount, according to Artificial Analysis. Several coding agents offered it for free at launch, including Kilo, OpenCode and Cline, for a limited or unspecified period.

For Opus 5.5, the only price in the available reports is the fast-mode rate of $8 input / $40 output. The roundup describes that rate as unchanged but does not restate units. Standard-tier list pricing for Opus 5.5 is not confirmed in these sources. Readers who need exact figures should check Anthropic's official pricing page.

Self-hosting changes the math again. NVIDIA's Scale AI case study for Skydance claims a 40x reduction in inference cost and 5x lower latency after post-training. That figure is vendor-reported and workload-specific.

Context, Modalities and Deployment

Nemotron 3.5 Super VL accepts text, images, documents, GUIs and video, and outputs text. Its context window is 1M tokens. The vision encoder is C-RADIOv4, and launch reports say there is no audio encoder. The NVIDIA technical blog positions it for coding, web research, document intelligence, cybersecurity and video understanding.

Local deployment is realistic on high-memory hardware:

Format	Size	Suggested memory
UD-IQ1_M (1-bit GGUF)	46.1GB	64GB
NVFP4 (4-bit)	79GB	96GB
UD-Q4_K_XL (4-bit GGUF)	86.9GB	128GB
Q8_0 (8-bit GGUF)	132GB	192GB

NVIDIA publishes an official single 128GB DGX Spark recipe. Upstream llama.cpp already supports the architecture, according to community posts.

Claude Opus 5.5 has no self-hosting path. Access runs through Anthropic's Claude Platform and cloud partners such as Microsoft Foundry. Teams that want to test Opus 5.5 through an API can also use the Claude Opus 5.5 model page. The context window and input modalities of Opus 5.5 are not confirmed in the sources reviewed for this page.

Which One Should You Use?

Choose Nemotron 3.5 Super VL if:

• You run high-volume, always-on agents where cost per task ($0.25) and throughput (196+ tokens/s) matter more than peak reasoning.
• Your data must stay on your own infrastructure, or you plan to post-train on your own documents, tools and policies with NVIDIA NeMo.
• Your workflow reads screenshots, PDFs, charts or video inside one 1M-token agent loop.

Choose Claude Opus 5.5 if:

• You need the highest general intelligence available today (58 on the Artificial Analysis index, ranked #1).
• Accuracy on open-ended, hard reasoning matters more than a 24x cost difference.
• You want a managed API with a fast-mode option and don't want to operate GPU serving yourself.

A practical pattern borrows NVIDIA's own routing advice. Send visual and document steps that are hard to get right to the strongest model, and push repetitive steps to the cheaper one. For other ways to frame Opus 5.5 against efficient challengers, see the Muse Wonder vs Claude Opus 5.5 comparison and the Gemini 4 Argon vs Claude Opus 5.5 breakdown.

Frequently Asked Questions

Is Nemotron 3.5 Super VL better than Claude Opus 5.5?

No, not on general intelligence. Opus 5.5 scores 58 on the Artificial Analysis Intelligence Index, and Nemotron 3.5 Super VL scores 26. Nemotron wins on speed (196 vs 97 tokens/s), cost per task ($0.25 vs $5.98), and open weights you can self-host and post-train.

Is Nemotron 3.5 Super VL cheaper than Claude Opus 5.5?

Yes, by a wide margin. Artificial Analysis lists Nemotron at $0.20 input and $0.575 output per million tokens and $0.25 per index task. Opus 5.5 costs $5.98 per task, about 24x more. A news roundup reports Opus 5.5 fast mode at $8 input and $40 output.

Is Nemotron 3.5 Super VL faster than Claude Opus 5.5?

Yes, about 2x on Artificial Analysis: 196 vs 97 output tokens per second. Opus 5.5 fast mode is reported to be up to 2.5x faster than standard Opus 5.5. No head-to-head fast-mode speed number has been published yet.

Did Nemotron 3.5 Super VL beat Claude Opus on benchmarks?

Only Claude Opus 5, and only after post-training, on five specialized task evaluations reported by VentureBeat. On the general Artificial Analysis index, Opus 5.5 leads 58 to 26.

Can I run Nemotron 3.5 Super VL locally instead of using Claude Opus 5.5?

Yes. Nemotron 3.5 Super VL ships open weights under OpenMDW-1.1. GGUFs range from 46.1GB (1-bit) to 132GB (8-bit), and NVIDIA publishes a single 128GB DGX Spark recipe. Opus 5.5 is available only through hosted platforms.

What is the context window of Nemotron 3.5 Super VL vs Claude Opus 5.5?

Nemotron 3.5 Super VL supports up to 1M tokens. The Claude Opus 5.5 context window is not confirmed in the sources reviewed for this comparison.

What to Watch Next

Three signals will change this page. The first is independent hands-on results, such as the DGX Spark context benchmarks Ivan Fioravanti has in progress. The second is any head-to-head of a post-trained Super VL against Opus 5.5 rather than Opus 5. The third is published component benchmarks and a speed figure for Opus 5.5 fast mode, which would fill the empty cells in the comparison table. NVIDIA's launch blog also points to more partner post-training results.

Try related models on Kie.ai: DeepSeek-V4.1-Flash, Gemini 4 Argon and Claude Haiku 5.5.